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Integrating Arts and Sciences inEducationIntegrating arts and sciences in education has been a topic of debate for many years. While some argue that the two disciplines should be kept separate, others believe that integrating them can lead to a more well-rounded education. In this response, we will explore the benefits of integrating arts and sciences in education from multiple perspectives, including the impact on student learning, the development of critical thinking skills, and the potential forinterdisciplinary collaboration. From the perspective of student learning, integrating arts and sciences in education can provide a more holistic approach to learning. By incorporating both disciplines, students can develop a deeper understanding of the world around them. For example, studying the science behind the colors of the sunset can be enhanced by creating a piece of art that reflects those colors. This hands-on approach to learning can engage students in a way that traditional classroom instruction may not. Furthermore, integrating arts and sciences can help students develop critical thinking skills. By engaging in both disciplines, students are encouraged to think creatively and analytically. This can help them develop problem-solving skills that are essential in both the arts and sciences. For example, a student studying the physics of sound may also be challenged to create a piece of music that demonstrates their understanding of those principles. This type of interdisciplinary approach can help students develop a more well-rounded set of skills that can be applied in a variety of contexts. In addition, integrating arts and sciences in education can also foster interdisciplinary collaboration. By bringing together students and educators from different disciplines, schools can create a more dynamic learning environment. For example, a science teacher and an art teacher may collaborate on a project that explores the intersection of their respective disciplines. This type of collaboration can help students see the connections between different areas of study and can help them develop a more holistic understanding of the world. However, there are also challenges to integrating arts and sciences in education. One of the main challenges is the need for educators to have a deep understandingof both disciplines. This can be difficult for some educators who may have specialized in one area or the other. Additionally, there may be logistical challenges in terms of scheduling and resources. For example, schools may need to invest in materials and equipment that are suitable for both arts and sciences, which can be costly. Despite these challenges, the benefits of integrating arts and sciences in education are clear. By providing a more holistic approach to learning, fostering critical thinking skills, and promoting interdisciplinary collaboration, schools can create a more dynamic and engaging learning environment for students. As we continue to explore the best practices for integrating arts and sciences in education, it is important to consider the potential impact on student learning and the development of well-rounded individuals.。
Integrating STEM Education into the CurriculumIntroductionSTEM education, which stands for Science, Technology, Engineering, and Mathematics, has gained increasing importance in modern education. It emphasizes the integration of these subjects to develop critical thinking, problem-solving skills, and innovation among students. In this document, we will explore the benefits of integrating STEM education into the curriculum and discuss practical strategies for implementation.Benefits of Integrating STEM Education1.Promoting Critical Thinking: STEM education encourages studentsto question, analyze, and evaluate information critically. It fosterslogical reasoning skills and helps students become active learners.2.Enhancing Problem-Solving Skills: The interdisciplinary nature ofSTEM education teaches students to approach problems holistically.They learn to apply scientific and mathematical concepts to real-world challenges.3.Encouraging Creativity and Innovation: STEM education nurturescreativity by providing opportunities for open-ended exploration and experimentation. Students are encouraged to think outside the box and develop innovative solutions.4.Preparing Students for Future Careers: By integrating STEMsubjects into the curriculum, students gain valuable knowledge and skills that are highly sought after in today's job market. Theydevelop a strong foundation for pursuing careers in science,technology, engineering, or mathematics.5.Fostering Collaboration: STEM education promotes collaborativelearning as students engage in group projects and solve complex problems together. This prepares them for teamwork in futureprofessional settings.Strategies for Implementing STEM Education1.Cross-Curricular Integration: Integrate STEM concepts acrossdifferent subjects rather than teaching them in isolation. Forexample, teach math through scientific experiments or usetechnology to enhance hands-on learning experiences in physics or biology classes.2.Project-Based Learning: Assign projects that require students toapply their knowledge from various STEM disciplines to solve real-world problems. This approach encourages critical thinking andproblem-solving skills development.3.Experiential Learning: Provide students with hands-on experiencesand opportunities to engage with STEM concepts in real-worldsituations. Field trips, guest speakers, and interactive workshops can bring STEM education to life.4.Teacher Professional Development: Offer professionaldevelopment programs for teachers to enhance their knowledgeand skills in delivering effective STEM education. This includestraining on integrating technology tools, designing inquiry-based lessons, and fostering a growth mindset among students.munity Partnerships: Collaborate with local businesses,universities, or research institutions to organize mentorshipprograms or guest lectures by professionals working in STEM fields.These partnerships can provide students with valuable insights into potential career paths.ConclusionIntegrating STEM education into the curriculum has numerous benefits for students. It develops critical thinking, problem-solving skills, creativity, and collaboration abilities while preparing them for future careers in science, technology, engineering, or mathematics. Byimplementing strategies such as cross-curricular integration, project-based learning, experiential learning, teacher professional development, and community partnerships, schools can create a comprehensive STEM educational experience that empowers students for success in the 21st century.。
knowledge distillation of large language model1. 引言1.1 概述在当前的人工智能领域中，大型语言模型的发展和应用日益受到关注。
这些模型在自然语言处理任务中取得了巨大的成功，如机器翻译、文本生成、问答系统等。
然而，由于这些模型庞大且参数众多，训练和应用过程需要消耗大量的计算资源和时间。
为了解决这一挑战，知识蒸馏成为了应对大型语言模型优化的一种有效方法。
该技术旨在通过将复杂庞大的语言模型的知识转移给更小、更高效的模型，以达到加速推理和降低资源消耗的目的。
1.2 文章结构本文将围绕知识蒸馏技术与大型语言模型进行优化展开论述。
文章首先介绍了知识蒸馏概念及其相关方法，并探讨了知识蒸馏在不同领域中的应用情况。
接下来，本文详细介绍了大型语言模型的基本背景以及其训练过程和实际应用案例。
在此基础上，文章将重点探讨基于知识蒸馏的大型语言模型优化方法。
我们将综述各种模型压缩技术，并深入剖析知识蒸馏与大型语言模型优化之间的关系。
同时，为了更好地阐述这一主题，本文还会通过实践案例分析，详细讲解如何利用知识蒸馏技术来优化大型语言模型。
最后，文章将总结所得要点，并对未来研究方向给出展望和建议。
1.3 目的本文的目标是全面探讨知识蒸馏技术在优化大型语言模型中的应用。
通过论述知识蒸馏方法和大型语言模型的基本概念、训练过程以及相关应用案例，旨在帮助读者深入理解知识蒸馏与大型语言模型优化之间的关联和意义。
同时，结合实践案例分析，为读者提供一些有益的实施指导和启示。
最终，我们希望能够为进一步探索和发展知识蒸馏技术以及优化大型语言模型提供有价值且具体可行的建议。
2. 知识蒸馏的概念：2.1 知识蒸馏定义知识蒸馏是一种模型压缩技术，旨在将较大、复杂的模型从教师模型转化为更小、更轻量级的学生模型。
通过这种方式，知识和经验可以被传递给学生模型，从而提高其性能和泛化能力。
这个概念最初由Hinton等人于2015年提出，并已在许多领域中得到广泛应用。
黑龙江科学HEILONGJIANG SCIENCE第12卷第5期2021年3月Vol. 12Mar. 2021环境类专业“三融合"培养模式创新与实践潘法康，张瑾，唐建设,伍昌年(安徽建筑大学环境与能源工程学院，合肥230601)摘要：为了培养创新型、复合型、应用型环境类专业人才,对环境类专业"三融合”培养模式的创新与实践展开探讨。
针对高等教育同质化倾向严重、结构性矛盾突出等问题，结合近年来对环境类专业人才培养模式的实践探索，提出了学科交叉融合、产教融合、科 研教学融合的"三融合”人才培养模式，提升了人才培养质量。
环境类专业"三融合”人才培养模式的探索和实践，取得了良好的教 学成果，提升了人才培养质量，为应用型高校人才培养和教育教学改革提供了参考。
关键词：环境类专业;三融合;培养模式中图分类号：G642.0文献标志码：A 文章编号：1674-8646(2021)05 -0038 -03Innovation and Practice of Three Integration Training Mode of Environmental SpecialtiesPan Fakang, Zhang Jin , Tang Jianshe , Wu Changnian(School of Environment and Energy Engineering , Anhui Jianzhu University , Hefei 230601 , China)Abstract : In order to cultivate innovative , compound and applied environmental talents as the goal orientation , thepaper explores the “ three integration " talent training mode of environmental specialty ・ According to the problems of severe homogenization and structural conflicts of the higher education , and through combining with the practicalexploration of talent training mode of environmental specialty in recent years , the research proposes the talent trainingmode of u three integration n , including integration of interdisciplinary, industry and teaching, scientific research andteaching , in order to improve talent training quality ・ The exploration and practice of u three integration n talent training mode of environmental specialty has achieved good teaching effect , improved talent training quality , and providedreferences for talent cultivation and teaching reform of applied colleges ・Key words : Environmental specialty ； Three integration ；高校要着重培养创新型、复合型、应用型人才,为高等教育人才培养指明方向⑴。
大模型知识蒸馏视觉模型英文文档：Title: Distilling Visual Models with Large ModelsIn recent years, the advancement of large models in the field of artificial intelligence has been remarkable.These models, with their massive scale and profound learning capabilities, have pushed the boundaries of what is possible in various domains, including computer vision.However, despite their remarkable performance, these large models are often computationally expensive, requiring significant resources for both training and inference.To address this issue, researchers have turned to knowledge distillation, a technique that allows the transfer of knowledge from a large, expensive model to a smaller, more efficient one.In the context of visual models, knowledge distillation has shown great potential in reducing the model size and improving inference speed without compromising too much on performance.One approach to knowledge distillation in visual models involves using a teacher-student framework.The teacher model, which is usually a large and powerful model, is trained on a large dataset to learn the underlying patterns and representations of the visual data.The student model, on the other hand, is a smaller and less resource-intensive modelthat is trained to mimic the teacher model"s behavior.Another technique that can be used in knowledge distillation is the use of attention mechanisms.These mechanisms help the student model focus on the most important parts of the input data, thereby reducing the amount of information that needs to be processed and improving efficiency.In conclusion, knowledge distillation is a promising technique for distilling visual models with large models.By transferring knowledge from a large and powerful model to a smaller and more efficient one, researchers can create visual models that are more suitable for deployment in real-world applications, where resources may be limited.中文文档：标题：利用大模型进行视觉模型知识蒸馏近年来，人工智能领域的大型模型取得了显著的进步。
简述howto模型和知识效应库解题流程英文版A Brief Introduction to the Problem-Solving Process of the HowTo Model and Knowledge Effect LibraryIn the realm of educational technology, the HowTo model and Knowledge Effect Library have emerged as powerful tools for enhancing learning outcomes. This article aims to provide a concise overview of the problem-solving process associated with these two innovative methodologies.The HowTo model is an instructional framework that guides learners through the process of achieving a specific goal or task. It emphasizes a step-by-step approach, breaking down complex tasks into manageable chunks. This model encourages active engagement and hands-on experience, allowing learners to apply their knowledge in practical settings.The Knowledge Effect Library, on the other hand, is a repository of educational resources designed to complementthe HowTo model. It provides a wealth of information, examples, and case studies that illustrate the application of knowledge in real-world scenarios. The library encourages learners to explore, experiment, and reflect, fostering a deeper understanding of the subject matter.When combined, the HowTo model and Knowledge Effect Library form a powerful problem-solving framework. Here's a step-by-step breakdown of the process:Identification of the Problem: The learner identifies a specific problem or challenge that requires solving. This could be an academic question, a real-world issue, or a personal goal.Formulation of a Plan: Using the HowTo model, the learner formulates a step-by-step plan to address the problem. This plan outlines the necessary steps, tools, and resources required for successful completion.Execution of the Plan: The learner proceeds to execute the plan, applying the knowledge and skills learned through theHowTo model. This phase involves hands-on experience and active engagement with the material.Analysis and Reflection: After executing the plan, the learner analyzes the results and reflects on the process. This allows them to identify any areas of improvement and gain insights into the effectiveness of their approach.Utilization of the Knowledge Effect Library: Throughout the process, the learner can refer to the Knowledge Effect Library for additional information, examples, and case studies. This resource acts as a valuable reference, enhancing the learner's understanding and ability to apply knowledge effectively.By leveraging the HowTo model and Knowledge Effect Library, learners can approach problems with a structured and informed mindset. This integrated approach not only improves learning outcomes but also fosters a culture of continuous learning and improvement.中文版简述howto模型和知识效应库解题流程在教育技术领域，howto模型和知识效应库已成为提高学习成果的有力工具。
Learning Models for Multi-Source Integration* Sheila Tejada Craig A. Knoblock Steven MintonUniversity of Southern California/ISI4676 Admiralty WayMarina del Rey, California 90292{tejada,knoblock,minton}@AbstractOne issue involved in accessing multiple heterogeneous information sources is how to integrate the retrieved data. SIMS, an information mediator, handles this problem by mapping the data in each information source into a common information or domain model. This model defines the relationships between the data in the different sources, so that the data can be integrated properly. Human experts who are familiar with the contents of the information sources are required to generate the model and perform the mapping of the sources. Automating this process of constructing the model of the information sources would be more convenient and efficient. Assuming that the structure of the information sources is a set of tables, the first step in this process is mining the tables to discover relationships in the data. These relationships are then used to further develop the model by helping to determine the necessary classes, superclass/subclass relationships, and associations.IntroductionBecause of the growing number of information sources available through the internet there are now many cases in which information needed to solve a problem or answer a question is spread across several information sources. In order to obtain the desired information, multiple sources need to be accessed, and the retrieved data then has to be integrated. An example to illustrate this problem of multi-source integration is when given two information sources, one source containing information about comic books and the other about super heroes, and you want to ask the question "Does Spiderman appear in a Marvel comic book?" To answer this question both of the information *This research reported here was suppoerted in part by Rome Laboratory of the Air Force Systems Command and the Advanced Research Projects Agency under Contract Number F30602-94-C-0210, and in part by an Augmentation Award for Science and Engineering Research Training funded by the Advanced Research Projects Agency under Contract Number F49620-93-1-0594. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing the officail opinion or policy of RL, ARPA, the U.S. Governments, or any person or agency connected with them.sources need to be accessed.In this situation it is necessary to have information about the relationships of the data within each source, as well as between sources, to properly access and integrate the data retrieved. The SIMS (Ambite et. al. 1995) information broker captures this type of information in the form of a model, called a domain model. Because all the information sources map into the domain model, the domain model serves as an interface which can provide the user access to multiple sources. The user specifies transactions to be performed using the terms of the domain model, and is not required to have knowledge about specific sources.In the next section we will describe the domain model in more detail, and then discuss our basic approach to address this problem of learning models for multi-source integration. Next, we will present our preliminary work that we have conducted on creating abstract descriptions of the data in order to mine for existing relationships. We will then provide a discussion of related work, and conclude by describing the future directions of our work.Model DescriptionThe diagram shown in Figure 1 is a partial model of both the Comic Book and Super Hero information sources.This model defines the relationships of the data within the two sources and also shows their interaction. The ovals in the diagram represent classes and the lines describe the relationships between the classes. There are two types of classes -- a basic class and a composite class. Members of a basic class are single-valued elements, such as strings or numbers; while the members of a composite class are objects which can have several attributes.In this example Comic Book Illustrator and Super Hero Creator are both basic classes which contain artists' names. Comic Book, Marvel Comic Book, and Super Hero are all represented as composite classes. The arrows represent the attributes of the objects, e.g. Artist, Title, Name. The Artist arrow between Super Hero and Super Hero Creator means that the values of that attribute belong to the set of names of Super Hero Creator. The thick lines in the model represent superclass/subclass relationships,e.g the names of Super Hero Creator are a subset of Comic Book Illustrator. The Contains relationship between Comic Book and Super Hero is an association link, which links a Comic Book object with a Super Hero object.This is like an attribute link, except that it connects two composite classes.Learning the ModelPresently, domain models are manually generated by human experts who are familiar with the data stored in the sources. Automation of this task would improve efficiency and the quality of the model. We have conducted preliminary work in automating the process of model construction. The basic idea for our approach is that we examine the data in the information sources to extract the relationships needed to create the domain model. Learning the model is an iterative process which involves user interaction. The user can browse the generated model to make corrections or specify an information source to be added or deleted. The model proposed to the user is generated by heuristics we have developed to derive the necessary relationships from the data. The user's input is incorporated in generating this model. The heuristics that we have applied involve creating abstract descriptions of the data which is described further in the next section.Abstract DescriptionsWe have assumed that the data is stored as tables, and that values within a column of a table are related to each other. An abstract description of a column of data defines the basic class for that collection of data. Properties or features of the data, such as type and range, are contained in the abstract descriptions. These descriptions are then used to help determine the superclass/ subclass relationships that exist between the basic classes. The descriptions can constrain the number of possible related classes. Potentially, all classes would need to be checkedfor a superclass/subclass relationship, but now only those classes that have similar descriptions are tested. One of the main reasons to generate these abstract descriptions of the data are so that they can be used to help determine composite classes and their relationships.Generating Abstract DescriptionsAn example of a data table is shown in Figure 2. A abstract description is determined for each column or attribute of the table. In this table the set of attributes are Name, Artist, and Super Power. A set of features is computed for each of these attributes which characterizes the basic classes to which each attribute belongs. The set of features that describes the basic classes also helps in discovering the relationships between basic classes. The relationships between basic classes should reflect the relationships that exist between the attributes.Name Artist Super PowerSpiderman Stan Lee Spider SenseSpawn Todd McFarlane ImmortalityCyclops Stan Lee X-ray Power ......Super Hero TableFigure 2Shown in Figure 3 is the table that contains the computed set of features for the attributes given in Figure 2. The set of features used to characterize each attribute in this example is Unique, Type, Subtype, Length, and Set of Values.Unique Type Subtype Length Set ofValues Name Yes String Alpha-betic6<=L<=9{Spawn,. . .} Artist No String Alpha-betic8<=L<=14{StanLee, . . .} SuperPowerYes String Alpha,Other11<=L<=12{SpiderSense.. .}Figure 3The Unique feature determines whether there are duplicate elements, e.g. the Name attribute does not contain duplicate values. The Type of an attribute is either string or number. The values for the feature Subtype are subtypes of string and number. The subtypes for number are integer and real. For string there are four basic subtypes: alphabetic, alphanumeric, numeric, and other. The value other refers to a string that has a least one character which is not a letter, a space or a number. The collection of the values of an attribute may be of one subtype or any combination of the four, as shown by the Super Power attribute.The Length or Range feature determines the range of the length of the set of strings or the range of the set of numbers in the attribute. For each of the attributes a set of their values are calculated by the Set of Values feature; therefore, duplicate values are removed, as is done for the Artist attribute. There are many other features which can be used to find regularities in the data, such as determining if the alphabetic strings of an attribute are words. These features represent only a subset.Relationships between Basic ClassesThe set of features shown in Figure 3 are the abstract descriptions of the attributes. These descriptions define the basic class of the attribute. The Set of Values feature provides all of the instances belonging to that basic class. The Type and Length features can be used to restrict the set of attributes that are related to a given attribute. For the attribute Name, the value for Type is string, and the value for Subtype is Alphabetic; therefore, Name can only be related to other attributes whose basic classes also have Type as string and a Subtype which includes Alphabetic. Based on the table in Figure 3 the set of related attributes whose basic classes overlap with Name's Type feature is {Artist, Super Power}. The same is now done for the Length feature of Name. There are no basic classes whose Length feature contains or is contained in the Name's Length feature, as shown by the comparisons of Length features depicted in Figure 4; therefore, the set of related attributes is empty.Fi g ure 4Next, the Set of Values feature is evaluated using the set of attributes produced by the intersection of the two previous sets of related attributes. This feature will decide the type of relationship that exists between the basic classes. The possible types of relationships are superclass/subclass, overlapping classes, and non-overlapping related classes. The Set of Values feature of Name is evaluated by testing the overlap of its values with those of an attribute in the related set.When the set of values of an attribute is a subset (or superset) of another attribute, then a superclass/subclass relationship exists between their basic classes. An example of this type of relationship can be seen in Figure 1 between the basic class Super Hero Creator of the Super Hero.Artis t attribute and the Comic Book Illustrator class of the Comic Book.Artist. The attributes are mapped into the model using this process which is repeated for each attribute in the table. For the attributes that have an overlapping or non-overlapping class relationship, a superclass of the two attributes needs to be created to properly represent their interaction. Further information needs to be computed in order to show that the set of these types of proposed relationships are valid.Related WorkThere has been similar work conducted in this area (Perkowitz & Etzioni 1995), but it has focused on learning only the attributes of the information sources. Their approach also assumes that it has instances as well as an initial model of the information to be learned from perspective information sources. Related work using mining techniques to learn a model of the information sources has been researched by Kero et al (Kero et al. 1995). Their work uses data mining to determine which attributes are related, while our work described in this paper is primarily interested in the types of the relationships that exist between the attributes.DiscussionThis is preliminary work on the automation of modeling information sources, which has focused mainly on discovering the relationships between basic classes. Further work still needs to be conducted to devise mechanisms for deciding when to generate a primitive superclass for the overlapping and the non-overlapping related basic classes. We are planning to apply statistical methods to assist in addressing these cases, as well as integrating a natural language knowledge base into this process to help determine whether classes are semantically related. The relationships between the basic classes will be useful in the later steps of the modeling process which are to determine the associations and superclass/subclass relationships between composite class.ReferencesKero, B., Russell, L., Tsur, S., & Shen W. An Overview of Database Mining Techniques. The KDD workshop in the 4th International Conference on Deductive and Object-Oriented Databases, Singapore, 1995.Ambite, J., Y. Arens, N. Ashish, C. Chee, C. Hsu, C. Knoblock, and S. Tejada. The SIMS Manual, Technical Report ISI/TM-95-428,1995.Perkowitz, M. & Etzioni, O. Category Translation: Learning to Understand Information on the Internet. The International Joint Conference on Artificial Intelligence, Montreal, 1995.。
第 21 卷 第 7 期2023 年 7 月太赫兹科学与电子信息学报Journal of Terahertz Science and Electronic Information TechnologyVol.21，No.7Jul.，2023高效无源互调建模仿真中的统计方法陈雄1,2，贺永宁3，于明1(1.南方科技大学电子与电气工程系，广东深圳518055；2.香港中文大学电子工程系，香港沙田999077；3.西安交通大学电子信息学部微电子学院，陕西西安710049)摘要：在现代大功率通信干扰问题中，无源器件的非线性杂散干扰机理最为复杂，在现代大功率高密度的通信系统中越来越受到重视。
面对无源器件的典型非线性效应之无源互调(PIM)问题，本文从PIM产生机理、建模角度梳理了近年来国内外普遍的PIM建模方法及关键步骤。
针对几种典型微观界面等效、PIM数值转换方法以及面向工况条件的微波部件建模问题，总结了现有的可行方法。
进一步提出了基于统计方法分析动态PIM区间预测分析方法的思路，该方法同样基于微观统计方法，对接触界面的不可具体量化的界面问题进行建模，最终获得工况条件下接触PIM的统计分布区间及其产物概率，为实际工况条件下微波部件PIM预测提供了一种大样本分析方法，为进一步提高实际微波部件的PIM稳定性提供了参考。
关键词：无源互调；非线性；微观界面分析；PIM统计预测中图分类号：TN98文献标志码：A doi：10.11805/TKYDA2022196Statistics methodology in efficient modeling and simulation ofPassive IntermodulationCHEN Xiong1,2，HE Yongning3，YU Ming1(1.Department of Electronic Electrical and Engineering，Southern University of Science and Technology，Shenzhen Guangdong 518055，China；2.Department of Electronic Engineering，The Chinese University of Hong Kong，Shatin Hong Kong 999077，China；3.School of Microelectronics，Faculty of Electronic and Information Engineering，Xi'an Jiaotong University，Xi'an Shaanxi 710049，China)AbstractAbstract：：In modern high power communication distortion problems, the nonlinear mixing distortion effect in passive devices is always the most difficult to investigate, and it has attracted great interests inthe modern high power and high density wireless communication system. Facing the typical nonlineareffect on passive device, Passive Intermodulation(PIM), this work concludes the common modelingmethods and the important steps in PIM mechanism researches in recent years. In particular, targeting tothe typical microscopic contact equivalence, PIM numerical transformation, and PIM prediction method inthe engineered cases, the available methods are concluded for present, while a statistics based PIMprediction method for engineered device with dynamic range is proposed for the first time. This methodcan model the specific contact sources by using statistic strategy. The PIM prediction can be expressed asa statistical interval with the probability of each PIM value. This method can provide a big sample data forthe engineered PIM prediction, and can guide the optimization of the PIM stability by controlling thedetailed parameters.KeywordsKeywords：：Passive Intermodulation；nonlinearity；microscopic interface analysis；statistical PIM prediction无源互调(PIM)是指当2个或多个大功率信号通过无源器件时，无源器件自身非线性特性在载波激励下产生新的互调频谱成分，一旦互调成分落入接收通带，尤其以距离信号频点最近的三阶交调信号为主，引起接收通文章编号：2095-4980(2023)07-0864-08收稿日期：2022-09-30；修回日期：2023-04-24基金项目：国家自然科学基金面上资助项目(62271240)；国家重点实验室基金2021-JCJQ-LB-006资助项目(6142411122115)第 7 期陈雄等：高效无源互调建模仿真中的统计方法道信号失真的一种现象[1]。
knowledge fusion of large languagemodelsKnowledge Fusion of Large Language ModelsLarge language models (LLMs) have emerged as a transformative technology in the field of artificial intelligence. These models, such as GPT-3 from OpenAI or BERT from Google, are capable of generating human-like text and understanding complex language patterns. However, one of the key challenges in their development is the integration and fusion of knowledge from various sources.Knowledge fusion, in the context of LLMs, refers to the process of combining and整合不同来源的知识 into a single, coherent representation. This involves integrating information from structured databases, unstructured text documents, and even expert knowledge. The goal is to create a model that can draw upon a rich and diverse knowledge base to generate more accurate and informative responses.To achieve this, LLMs rely on a range of techniques and methods. One approach is the use of transfer learning, where a model trained on a large corpus of text is fine-tuned on a specific task or domain. This allows the model to acquire knowledge from a general-purpose dataset and then adapt it to a more focused context.Another approach is the integration of external knowledge sources, such as knowledge graphs or ontologies. These structured representations of knowledge can provide valuable information to the model, enabling it to understand relationships and connections between different concepts and entities.The fusion of knowledge in LLMs is also aided by the use of advanced architectures and algorithms. For example, transformer-based models like GPT-3 employ self-attention mechanisms that allow them to capture complex dependencies between words and phrases. This enables the model to understand context more effectively and generate more coherent and informative text.In summary, knowledge fusion is a crucial aspect of large language modeldevelopment. By integrating diverse sources of knowledge and leveraging advanced techniques and algorithms, we can create models that are not only capable of generating human-like text but also possess a rich and comprehensive understanding of the world. This has the potential to transform a wide range of applications, from language understanding and generation to question answering and knowledge reasoning.。
Education reform is a topic of significant importance in todays world,as it directly impacts the future of society and the development of individuals.Here are some key points to consider when discussing educational reform:1.Curriculum Modernization:One of the primary aspects of educational reform is updating the curriculum to reflect current knowledge and skills required in the modern world.This includes integrating technology,critical thinking,and problemsolving skills into the learning process.2.Teacher Training and Development:Teachers are the backbone of the educational system.Reforms should focus on continuous professional development for teachers, providing them with the necessary tools and training to adapt to new teaching methodologies and technologies.3.Assessment and Evaluation:Traditional assessment methods often focus on rote memorization and standardized testing.Reforms should encourage a more holistic approach to evaluation,considering creativity,collaboration,and practical application of knowledge.4.Inclusivity and Accessibility:Education should be accessible to all,regardless of socioeconomic status,physical abilities,or learning differences.Reforms should aim to remove barriers and provide equal opportunities for all students to succeed.5.StudentCentered Learning:Shifting the focus from teachercentered to studentcentered learning can empower students to take charge of their own education.This involves personalized learning plans,projectbased learning,and fostering a sense of curiosity and selfmotivation.6.Technology Integration:With the rapid advancement of technology,it is crucial to integrate digital tools and resources into the classroom.This can enhance learning experiences,provide access to a wealth of information,and prepare students for the digital age.7.Lifelong Learning:Education reform should promote the concept of lifelong learning, encouraging individuals to continue their education and skill development beyond formal schooling.This can be achieved through community programs,online courses,and flexible learning opportunities.8.Global Perspectives:In an increasingly interconnected world,it is important for educational reform to include a global perspective.This involves teaching about differentcultures,global issues,and fostering international understanding.9.Funding and Resources:Adequate funding is essential for implementing educational reforms.This includes investing in infrastructure,technology,and teacher salaries to ensure quality education for all.munity and Parental Involvement:The success of educational reform often depends on the support and involvement of the community and parents.Engaging stakeholders in the decisionmaking process can lead to more effective and sustainable reforms.By addressing these areas,educational reform can lead to a more equitable,effective,and forwardthinking education system that prepares students for the challenges and opportunities of the21st century.。
Integrating Knowledge Modeling and Multi-Agent SystemsMario G´o mez and Enric PlazaArtificial Intelligence Research Institute-Spanish Scientific Research CouncilCampus UAB08193BellaterraBarcelona,Spain{mario,enric}@iiia.csic.esAbstractThis paper outlines ORCAS,a framework for openMulti-Agent Systems(MAS)that maximizes the reuseof agent capabilities through multiple application do-mains,and supports the automatic,on-demand configu-ration of agent teams according to stated problem re-quirements.Considerable effort has been devoted tothe applicability of the framework,which resulted in theimplementation of an infrastructure to develop and de-ploy cooperative MAS.This infrastructure explores theidea of configuring an electronic institution on-the-flytofit the specific requirements of each problem to besolved by a group of agents.IntroductionOpen MAS require a new way of managing and integrating agent capabilities based on middleware:connectivity soft-ware that enables multiple processes running on one or more machines to interact across a network.When implemented in MAS,the middleware layer is usually provided by mid-dle agents(Decker,Sycara,&Williamson1997),such as matchmakers(Decker,Williamson,&Sycara1996),facil-itators(Erickson1996;Genesereth&Ketchpel1997)and brokers(Nodine,Bohrer,&Ngu1999).Typically,the func-tion of a middle agent is to pair requesters with providers that are suitable for them,a process called matchmaking. Matchmaking is the process of verifying whether the specification of an agent capability“matches”the specifi-cation of a request to do something(a task to achieve): two specifications“match”if certain relation holds between them,usually a capability being able to achieve some task. Since matchmaking compares the specification of requests and advertisements,both providers and requesters must share a common language to describe them.This language is usually called an Agent Capability Description Language (ACDL).Semantic matchmaking,which is based on the use of shared ontologies to annotate agent capabilities(Guar-ino1997),improves the matchmaking process and facilitates interoperation.However,the reuse of existing capabilities over new application domains is still difficult because capa-bilities are usually associated to a specific application do-main.Copyright c 2005,American Association for Artificial Intelli-gence().All rights reserved.Moreover,in addition to capability discovery throughmatchmaking,we want an ACDL to support other activitiesinvolved in MAS interoperation,namely:invocation,com-position(team-design),team-formation and coordination.•Invocation:an agent willing to invoke the capability pro-vided by another agent must provide the input data in anappropriate format and using a shared interaction proto-col.•Composition:refers to the aggregation of several capabil-ities to achieve a global team goal.This process requires a combination of matchmaking,capability selection,and verification of whether the aggregated functionality sat-isfies the specification of the global goal.We call this process Team Design.•Team Formation:is the process of allocating tasks to agents,according to the constrains determined during the Team Design process.Team members can be selected among several candidate agents,either in a distributed or centralized manner,and agents must agree upon the in-teraction protocols to coordinate during the cooperative activity.•Coordination:team members must synchronize their ac-tions so as to avoid deadlocks and effectively cooperate. We want an ACDL to support both requesters and providers through all these activities.Our main goals are to extend matchmaking so as to maximize capability reuse,and to support the automatic composition of capabilities accord-ing to stated problem requirements.In a wide sense of the word,our focus is on the reuse issue,that we define as how to reuse an agent capability for different tasks,across sev-eral application domains,and interacting with other capabil-ities provided by different,probably heterogeneous agents. Therefore,in order to maximize the reuse of agent capa-bilities,we have explored the potential of the knowledge-modelling stance and the ideas brought about by the compo-nential approach to software development.The result of our work is ORCAS a multi-layered framework for the design, development,and deployment of Cooperative MAS in open environments.But instead of going through the ORCAS framework layer by layer,this paper describes the corner-stones of the ORCAS framework transversally.The aspects of the ORCAS framework we focus hereinare the ORCAS model of the Cooperative Problem Solving(CPS)process,and the ORCAS Agent Capability Descrip-tion Language (ACDL).Overview of the ORCAS frameworkThis section provides an overall view of the framework and reviews the most important concepts needed to under-stand the other sections.The reader is referred to (G´o mez 2004)for a complete,detailed description of the framework.ORCAS has three layers,namely:the Knowledge Mod-elling Framework,the Operational Framework and the In-stitutional Framework:1.The Knowledge Modelling Framework (KMF)proposes a conceptual and architectural description of problem-solving systems from a knowledge-level view,abstract-ing the specification of components from implementation details.In addition,the KMF incorporates a bottom-up design process to configure a system out of elementary components,until a system configuration is found that satisfies some global requirements.This process is used to design the organization of a team and the competence required for each team role during the problem-solving process.2.The Operational Framework deals with the link between the specification of components in the KMF,and the oper-ational aspects of Multi-Agent Systems.This framework comprehends an extension of the KMF to obtain a full-fledged Agent Capability Description Language,together with a new model of the Cooperative Problem Solving process that includes a Team Design stage prior to the Team Formation stage.3.The Institutional Framework describes an implemented infrastructure for developing and deploying Multi-Agent Systems configurable on-demand,according to the the re-quirements of the problem athand.Figure 1:The three layers of the ORCAS framework Figure 1shows the three layers as a pyramid made of three blocks.The block at the bottom corresponds to the more abstract layer,while upper blocks corresponds to increas-ingly implementation dependent layers.Therefore,develop-ers and system engineers can decide to use only a portion of the framework,starting from the bottom,and modifying or changing the other frameworks according to its preferences andneeds.Figure 2:The ORCAS Abstract ArchitectureKnowledge Modelling FrameworkThe ORCAS Knowledge Modelling Framework (KMF)proposes a conceptual description of Multi-Agent Systems at the knowledge level (Newell 1982),abstracting the spec-ification of components from implementation details.The purpose of the Knowledge Modelling Framework (KMF)is twofold:on the one hand,the KMF is a conceptual tool to guide developers in the analysis and design of Multi-Agent Systems in a way that maximizes capability reuse across dif-ferent domains;on the other hand,the KMF provides the ba-sis for an Agent Capability Description Language (ACDL)supporting the automatic,on-demand configuration of agent teams according to stated problem requirements.The ORCAS KMF is based on the Task-Method-Domain (TDM)paradigm prevailing in existing Knowledge Mod-elling frameworks.This paradigm distinguishes between three classes of components:tasks ,problem-solving meth-ods (PSM)and domain models .In ORCAS there are tasks and domain models,while PSMs are replaced by agent capa-bilities,playing the same role as PSMs,but including agent specific features concerning communication and coordina-tion.Adopting this KMF we expect the ORCAS Abstract Architecture to provide an effective organization for con-structing libraries with large “horizontal cover”(Breuker &Van de Velde 1994;Valente,Van de Velde,&Breuker 1994;Motta 1999),thus maximizing reusability and avoiding the brittleness of monolithic libraries (Motta et al.1999).A task is a functional description of a type of problem to be solved.A task is functionally characterized by input roles ,output roles ,and the relationship between them,which is specified as a set of preconditions and postconditions .A capability describes a particular method for solving problems with some specific properties.A capability is specified from a functional viewpoint by stating the input roles ,output roles ,preconditions and posconditions .In ad-dition,a capability can specify the type of domain knowl-edge (knowledge-roles )it requires,and some properties that have to be fulfilled by the domain knowledge to sensibly ap-ply that capability (assumptions ).There are two types of capability:task-decomposer and skill .While skills are used to describe primitive,atomic rea-soning steps,task-decomposers are used to describe com-plex reasoning methods that decompose a problem into sev-eral subtasks.Finally,a domain model (DM)specifies the concepts,re-lations and properties characterizing the knowledge from certain application domain.The ORCAS KMF extends matchmaking in two ways (G´o mez &Plaza 2004):first,in addition to provide its own version of a task-capability matching ,ORCAS introduces a capability-domain matching to decide whether a capability is suitable to be applied over certain application domain;and second,ORCAS addresses the composition of capabilities on-demand,according to the requirements of the problem at hand.We regard the composition of capabilities as a “bottom-up design problem”(Hafedh Mili &Mili 1995),which in agent terms would be rewritten as:given a set of requirements,find a set of agent capabilities whose combined competence and available knowledge satisfies those requirements .The main difficulty to solve that problem is how to decompose the requirements in such a way as to yield component spec-ifications.Our approach to this problem is to use a search process over the space of possible configurations.In OR-CAS ,the bottom-up design process is called Team Design,and the result is a task-configuration ,a hierarchical decom-position of a task into subtasks,and capabilities bound to tasks according to matching relations,in such a way that the resulting task-configuration satisfies the global problem re-quirements.Figure 3:Task-configuration exampleFigure 3shows an example of a task-configuration for the Information-Search task,which is used within the WIM application.This task is being decomposed into four tasks by the Meta-search task-decomposer:Elaborate-query ,Customize-query ,Retrieve and Aggregate ,which is fur-ther decomposed by the Aggregation capability in two sub-tasks:Elaborate-items and Aggregate-items .The ex-ample shows some skills requiring domain knowledge,e.g.the Query-expansion-with-thesaurus requires a thesaurus (e.g.MeSH ,a medical thesaurus),and the Retrieval and Query-customization skills require a description of infor-mation sources.Operational FrameworkThe Operational Framework describes a mapping from con-cepts in the Knowledge-Modelling Framework to concepts from Multi-Agent Systems.Specifically,the Operational Framework describes how a composition of capabilities rep-resented at the knowledge-level can be operationalized by a customized team of agents;in other words,how to form a team of agents able to carry on the execution of a particu-lar composition of capabilities,over a particular application domain.The Operational Framework proposes a hierarchical model of teamwork that is straightforwardly derived from the hierarchical decomposition of tasks into subtasks that is the backbone of the TDM paradigm.This model of team-work is embedded within a complete model of the Coopera-tive Problem Solving process that covers all the stages,from the specification of a problem to be solved to the activities carried on by agents willing to solve it.In order to effectively use a KMF in open agent envi-ronments,a capability description language should include some way of specifying the communication and the coor-dination mechanisms required by agents to cooperate.Our approach to describe such aspects of a capability is based on the macro-level (societal)aspects of agent societies,which is focused on the communication and the observable behav-ior of agents,rather than adopting a micro-level (internal)view on individual agents.In particular,we are using con-cepts from the electronic institutions formalism to describe such aspects,as explained in the next section.Institutional FrameworkAn electronic institution (e-Institution),is a “virtual place”designed to support and facilitate certain goals to the human and software agents concurring to that place (Noriega 1997;Rodr´ıguez-Aguilar 1997).Since these goals are achieved by means of the interaction of agents,an e-institution provides the social mediation layer required to achieve a successful interaction:interaction protocols,shared ontologies,com-munication languages and social behavior rules.The ORCAS Institutional Framework devises a institu-tional model covering all the stages of the ORCAS Coop-erative Problem Solving process:the ORCAS e-Institution,a platform for developing and deploying cooperative MAS that supports both providers and requesters of capabilities along the different stages of the ORCAS model of the CPS process.The e-Institutions formalism was originally conceived to deal with static organizations encompassing a fixed role structure and fixed interaction protocols,while the ORCAS institutional framework (both conceptually and in the imple-mented agent platform)is an electronic institution that al-lows other institutions to be configured on-the fly.The point is that in the ORCAS platform,each team formed to solve a problem implies that a new electronic institution is com-posed on demand out of the interaction protocols agents are equipped with,and that institution is used as the social me-diation layer required by team members to effectively com-municate and coordinate during the CPS activity.The integration of the knowledge-modelling stance and the electronic institutions formalism within the ORCAS e-Institution favors the development of highly configurable and reusable MAS in open environments.Remark that in addition to implement an agent infrastruc-ture using the electronic institutions formalism,we are us-ing the concepts proposed by the e-Institutions approach to specify the communication and coordination mechanisms of individual agents.In order to demonstrate the applicability of the ORCAS framework,we have built WIM ,a MAS-based configurable application to search bibliographic information in the In-ternet (G´o mez &Abasolo 2003;G´o mez 2004).WIM is an e-Institution that results of linking a library of informa-tion search and aggregation (ISA)capabilities provided by agents,and domain knowledge from medicine,and more specifically,Evidence-Based Medicine (EBM).Figure 4outlines the architecture of the WIM application.The OR-CAS e-Institution provides the mediation service for agents to communicate and cooperate.The institution provides a yellow pages service (through a librarian agent)where prob-lem solving agents register their capabilities.The capabili-ties in the library are link to some domain models character-izing the application domain (EBM).User requests to per-form search tasks solve problem are received through a Per-sonal Assistant agent that expresses them using the ISA on-tology.This agent acts as mediator between the user and the institutional agents providing the services required to carry on the CPS process:to find a valid task-configuration (Team Design),to allocate tasks to agents (Team Formation),and to coordinate individual agent behaviors to achieve the global task cooperatively(Teamwork).The figure depicts also the existence of wrappers,ad-hoc elements that are used to agentify external information sources,making them acces-sible toagents.Figure 4:Task-configuration exampleThe ORCAS model of the CooperativeProblem-Solving processMost of the research done in the field of cooperative MAS fits into one or more or the stages of the CooperativeProblem-Solving process as presented in (Wooldridge &Jennings 1994),which consists of four stages:recognition ,team formation ,planning and execution .Although the proposers of this model believe many in-stances of the CPS process exhibit these stages in some form (either explicitly or implicitly),they stress that the model is idealized.In other words,there are cases that the model can-not account for (Wooldridge &Jennings 1999).Since team formation is not guided by a preplan to achieve the overall goal,but is just a commitment to joint action,then neither the agents joining a team (committing to carry on joint ac-tion)are guaranteed to play one role in the team once a plan was decided at the subsequent planning stage,nor the result-ing team assures that a global plan can be found.The SharedPlans theory (Grosz &Kraus 1996)and the frameworks based on it,e.g.(Giampapa &Sycara 2002),have emphasized the need for a common,high-level team model that allows agents to understand the requirements to achieve a global team goal and select a plan.Team plans are used by agents to acquire goals,to identify roles and to relate individual goals to team goals.A plan allows the ini-tiator of the team formation process to know which are the subgoals and (optionally)the actions or capabilities required to achieve each subgoal.Therefore,the initiator of a CPS process can use an initial plan to guide the team formation process (Tidhar,Rao,&Sonenberg 1996).However,there is still another limitation of most planning frameworks used in real,implemented MAS:plans are tightly bound to a very specific domain and generic tasks.Consequently,plans are hardly reusable for different domains,and cannot be adapted to fit specific requirements of the problem at hand (e.g.dif-ferent users may prefer different ways of achieving a task,and thus different capabilities may be preferable depending on the user preferences).Finally,most planning-based MAS devise an internal per-spective of agents,whose internal state is used as the basis for evaluating the cooperative behavior.Concerning this is-sue,though we recognize there are well founded reasons to adopt an internal perspective in several contexts,we think a external view has some notable advantages under certain circumstances;in particular,it is more appropriate for open systems because it avoids imposing a model of the internal agent architecture to external agent developers.Summing up,we address some requirements for devel-oping Cooperative MAS in open environments that are not entirely covered by previous models of the CPS process:(1)the need for initial plans to guide the team formation pro-cess;(2)the consideration of the user preferences and spe-cific problem requirements as a constrain over the compe-tence of a team;and (3)an external view centered on ob-servable events,rather than an internal view imposing a par-ticular agent architecture.As a result of our work upon these issues we have con-ceived a new model of the CPS process with four sub-processes (Figure 5),namely Problem Specification,Team Design,Team Formation and Teamwork.The Problem Specification process produces a specifica-tion of problem requirements to be met by a team,includ-ing a description of the application domain (a collection ofFigure5:Overview of the ORCAS Cooperative Problem Solving process.domain models)and the problem data to be used during the Teamwork process.The Team Design process uses the prob-lem requirements to build a task-configuration—a composi-tion of tasks,capabilities and domain models.The resulting task-configuration is used during the Team Formation pro-cess to allocate tasks and subtasks to agents,and instruct agents to ensure that the requirements of the problem are achievable.Finally,during the Teamwork process,team members try to solve the problem cooperatively,following the instructions received during Team Formation,and thus complying with the specific requirements of the problem at hand.The ORCAS model of the CPS process should not be un-derstood as afixed sequence of steps.Actually,we have im-plemented strategies that interleave Team Design and Team Formation with Teamwork,thus enabling distributed con-figuration,lazy configuration and dynamic reconfiguration of teams on runtime(G´o mez2004).The ORCAS Agent Capability DescriptionLanguageThe functional description of a capability as provided in the Knowledge-Modelling Framework enables the automated discovery and the composition of capabilities,without tak-ing into account communication and coordination require-ments.Nonetheless,the invocation of capabilities and the interoperation of multiple agents are not supported by the functional description of a capability.In order to deal with these activities,we have to include information concerning the communication requirements of an agent over the capa-bilities he provides,and the operational description(control and dataflow)of tasks-decomposers.Figure6shows the main concepts specifiable in the ORCAS ACDL.In ORCAS,both the communication and the operational description of a capability are described using concepts from the electronic institutions formalism(Esteva et al.2001), which adopts an external view on agents.Specifically,OR-CAS uses scenes to describe the communication require-ments of an agent over a particular capability,and perfor-mative structures to specify the operational description of a task-decomposer.Since we are using those concepts in a new way,a brief review of the electronic institutions con-cepts used in ORCAS is pertinentnow:Figure6:Main features of the ORCAS ACDL1.Agent roles:Agents are the players in an electronicinstitution,interacting by the exchange of speech acts, whereas roles are standardized patterns of behavior re-quired by agents playing part in given functional relation-ships.2.Dialogic framework:A dialogic framework determinesthe valid illocutions that can be exchanged among the agents participating in an electronic institution,which in-volves a vocabulary(ontology)and some agent commu-nication language(ACL).munication scenes:A scene defines an interactionprotocol among a set of agent roles,and using some di-alogic framework.4.Performative structure:A performative structure is a net-work of connected scenes that captures the relationships among scenes.A performative structure constrains the paths agents can traverse to move from one scene to an-other,depending on the roles they are playing. CommunicationAgent capabilities should be specified independently of other agents in order to maximize their reuse and facilitate their specification by third party agent developers.In the general case,agent developers do not know a priori the tasks that could be achieved by a particular capability,neither the domains they could be applied to.As a consequence,the team roles an agent could play using a capability are not known in advance,thus the scenes used to specify the com-munication requirements of an agent over certain capabil-ity cannot be specified in terms of specific team-roles,but in terms of abstract,generic problem solving roles.Since ORCAS teams are designed in terms of a hierarchical de-composition of tasks into subtasks,then teamwork in OR-CAS is straightforwardly organized as a hierarchy of team-roles.Some positions within a team(team-roles)are bound to a task-decomposer,thus the agents playing those team-roles are responsible of delegating subtasks to other agents, receiving the results,and performing intermediate data pro-cessing between subtasks.In such an scenario,we can estab-lish an abstract communication model with two basic roles: coordinator,which is adopted by an agent willing to decom-pose a task into subtasks,and operator,which is adopted by the agent having to perform a task on demand,using the data provided by another agent that acts as coordinator of a top-level taskFigure 7:Example of a communicationsceneFigure 8:Choosing scenes during Team Formation Figure 7shows an example of a scene depicting the com-munication requirements of an agent over a capability.This example shows a typical request-inform protocol in terms of the ORCAS generic roles:coordinator (requester)and operator (informer).Operational descriptionThe ORCAS approach to specify the operational descrip-tion of a task-decomposer is based on performative struc-tures,with some distinctive features.In ORCAS ,as in the e-institutions formalism,each scene within a performa-tive structure corresponds to an interaction protocol.How-ever,in ORCAS the scenes within a performative structure are not instantiated beforehand.Actually,these scenes are instantiated during the team-formation process using as a source the set of communication scenes shared by the agents willing to interactEach scene corresponds to the communication required to solve a subtask,which implies an agent acting as coordina-tor invoking the capability provided by another agent acting as operator.Both the coordinator and the operator must ad-here to the same scene in order to communicate,and as a consequence,the scene must be chosen out of the scenes supported by both agents (see Figure 8).Since agents are selected dynamically during the Team Formation process,then the scenes used within ORCAS performative structures must also be chosen dynamically,before starting Teamwork.Figure 9shows an example of a performative structure specifying the operational description of the Aggregation task-decomposer,which decomposes a task into two sub-tasks:Elaborate-items and Aggregate-items .Therefore,this performative structure has two scenes (in addition to the Start and End scenes),one for each subtask,and three roles:x,y,z .There is one role of type coordinator (x )tobe played by the agent applying the task-decomposer,and as many operators as subtasks.In the example there are two operators,one (y )participating in the Elaborate-items (EI)scene,and another (z )participating in the Aggregate-Items (AI)scene.Notice that the coordinator (x )is the same in both scenes,it enters first the EI scene,and can enter the AI scene only after finishing the EIscene.Figure 9:Task-decomposer operational description So far,we have addressed the performative structure as-sociated to a single task-decomposer,but what about the op-erational description of an entire team?The ORCAS organization of a team adheres to the hier-archical decomposition of task into subtasks embodied by a task-configuration:starting from a top team-role,each team-role associated to a task-decomposer introduces a set of team-roles subordinated to it.Since each task-decomposer has an operational description described by a performative structure,therefore,the operational description of a team can be modelled as a nested structure of performative structures (Figure 10shows an example).There is one performative structure for each task-decomposer,starting from the team-role associated to the root task of a task-configuration (theteam-leader).Figure 10:Teamwork as a nested structure of performative structuresFigure 10sums up the specification of the teamwork ac-tivity as a nested structure of performative structures.No-tice that there is a performative structure for each task-decomposer in the task-configuration,and there is one scene for each task.Performative structures embody which roles。
