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Integrating knowledge modeling and multiagent systems

Integrating knowledge modeling and multiagent systems
Integrating knowledge modeling and multiagent systems

Integrating Knowledge Modeling and Multi-Agent Systems

Mario G′o mez and Enric Plaza

Arti?cial Intelligence Research Institute-Spanish Scienti?c Research Council

Campus UAB

08193Bellaterra

Barcelona,Spain

{mario,enric}@iiia.csic.es

Abstract

This paper outlines ORCAS,a framework for open

Multi-Agent Systems(MAS)that maximizes the reuse

of agent capabilities through multiple application do-

mains,and supports the automatic,on-demand con?gu-

ration of agent teams according to stated problem re-

quirements.Considerable effort has been devoted to

the applicability of the framework,which resulted in the

implementation of an infrastructure to develop and de-

ploy cooperative MAS.This infrastructure explores the

idea of con?guring an electronic institution on-the-?y

to?t the speci?c requirements of each problem to be

solved by a group of agents.

Introduction

Open 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 speci?cation of an agent capability“matches”the speci?-cation of a request to do something(a task to achieve): two speci?cations“match”if certain relation holds between them,usually a capability being able to achieve some task. Since matchmaking compares the speci?cation 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 dif?cult because capa-bilities are usually associated to a speci?c application do-main.

Copyright c 2005,American Association for Arti?cial Intelli-gence(https://www.doczj.com/doc/6816281449.html,).All rights reserved.

Moreover,in addition to capability discovery through

matchmaking,we want an ACDL to support other activities

involved 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 an

appropriate 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 veri?cation of whether the aggregated functionality sat-is?es the speci?cation 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 de?ne 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 herein

are the ORCAS model of the Cooperative Problem Solving

(CPS)process,and the ORCAS Agent Capability Descrip-tion Language (ACDL).

Overview of the ORCAS framework

This 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 speci?cation of components from implementation details.In addition,the KMF incorporates a bottom-up design process to con?gure a system out of elementary components,until a system con?guration is found that satis?es 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 speci?cation 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-?edged 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 con?gurable on-demand,according to the the re-quirements of the problem at

hand.

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 and

needs.

Figure 2:The ORCAS Abstract Architecture

Knowledge Modelling Framework

The ORCAS Knowledge Modelling Framework (KMF)proposes a conceptual description of Multi-Agent Systems at the knowledge level (Newell 1982),abstracting the spec-i?cation 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 con?guration 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 speci?c 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 speci?ed as a set of preconditions and postconditions .A capability describes a particular method for solving problems with some speci?c properties.A capability is speci?ed 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 ful?lled 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)speci?es 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):?rst,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,?nd a set of agent capabilities whose combined competence and available knowledge satis?es those requirements .The main dif?culty to solve that problem is how to decompose the requirements in such a way as to yield component spec-i?cations.Our approach to this problem is to use a search process over the space of possible con?gurations.In OR-CAS ,the bottom-up design process is called Team Design,and the result is a task-con?guration ,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-con?guration satis?es the global problem re-

quirements.

Figure 3:Task-con?guration example

Figure 3shows an example of a task-con?guration 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 Framework

The Operational Framework describes a mapping from con-cepts in the Knowledge-Modelling Framework to concepts from Multi-Agent Systems.Speci?cally,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 speci?cation 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 Framework

An 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′?guez-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 ?xed role structure and ?xed 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 con?gured on-the ?y.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 con?gurable 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 con?gurable 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 speci?cally,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 ?nd a valid task-con?guration (Team Design),to allocate tasks to agents (Team Formation),and to coordinate individual agent behaviors to achieve the global task cooperatively(Teamwork).The ?gure depicts also the existence of wrappers,ad-hoc elements that are used to agentify external information sources,making them acces-sible to

agents.

Figure 4:Task-con?guration example

The ORCAS model of the Cooperative

Problem-Solving process

Most of the research done in the ?eld of cooperative MAS ?ts into one or more or the stages of the Cooperative

Problem-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 speci?c domain and generic tasks.Consequently,plans are hardly reusable for different domains,and cannot be adapted to ?t speci?c 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-ci?c 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 Speci?cation,Team Design,Team Formation and Teamwork.

The Problem Speci?cation process produces a speci?ca-tion of problem requirements to be met by a team,includ-ing a description of the application domain (a collection of

Figure5: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-con?guration—a composi-tion of tasks,capabilities and domain models.The resulting task-con?guration 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 speci?c requirements of the problem at hand.

The ORCAS model of the CPS process should not be un-derstood as a?xed sequence of steps.Actually,we have im-plemented strategies that interleave Team Design and Team Formation with Teamwork,thus enabling distributed con-?guration,lazy con?guration and dynamic recon?guration of teams on runtime(G′o mez2004).

The ORCAS Agent Capability Description

Language

The 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 data?ow)of tasks-decomposers.Figure6shows the main concepts speci?able 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.Speci?cally,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 pertinent

now:

Figure6:Main features of the ORCAS ACDL

1.Agent roles:Agents are the players in an electronic

institution,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 determines

the 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).

https://www.doczj.com/doc/6816281449.html,munication scenes:A scene de?nes an interaction

protocol 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. Communication

Agent capabilities should be speci?ed independently of other agents in order to maximize their reuse and facilitate their speci?cation 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 speci?ed in terms of speci?c 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 task

Figure 7:Example of a communication

scene

Figure 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 description

The 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 interact

Each 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 )to

be 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 ?rst the EI scene,and can enter the AI scene only after ?nishing the EI

scene.

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-con?guration: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-con?guration (the

team-leader).

Figure 10:Teamwork as a nested structure of performative structures

Figure 10sums up the speci?cation 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-con?guration,and there is one scene for each task.Performative structures embody which roles

are required to play each scene,and the dependencies among scenes,e.g.some scenes must be?nished before starting an-other scene,other scenes can be performed in parallel,and some scenes can be instantiated multiple times.

The teamwork process follows the hierarchical structure of a task-con?guration,decomposing a task into subtasks when there is a task-decomposer.The teamwork process starts with a team-leader having to apply a task-decomposer. The team-leader initiates teamwork by following the perfor-mative structure speci?ed in the operational description of its task-decomposer(Metasearch in Figure10).Each scene within the performative structure refers to a communication scene to be played by the team-leader acting as coordinator and another agent allocated a subtasks,and playing the oper-ator role.Since some of the subtasks may be bound to task-decomposers,a new performative structure must be carried over for each new task-decomposer.The?rst performative structure(the one initiated by the team-leader)cannot?nish until the subsequent performative structures are?nished,in other words,performative structures are nested.

In Figure10,there are two task-decomposers (Metasearch and Aggregate),and thus there are two performative structures,one(Aggregate))within the other.Each time a new team is formed complying with a task-con?guration,a new structure of nested performative structures is composed and their scenes instantiated.We regard this structure as a dynamic institution,since it is con?gured on-the-?y,out of the communication ca-pabilities of agents and the operational descriptions of tasks-decomposers.

Conclusions

The ORCAS framework explores the feasibility of the Prob-lem Solving Methods(PSMs)approach to describe agent ca-pabilities in a way that maximizes their reuse across multi-ple application domains.However,since PSMs were not de-signed having agents in mind,we have had to adapt them to deal with agent speci?c concepts such as communica-tion,coordination and cooperation.Having situated our framework in the context of mediated architectures for co-operation,we have extended PSMs to obtain a full-?edged Agent Capability Description Language including not only the functional description of a capability,but also the com-munication requirements and the operational description of capabilities.We have adapted electronic institutions con-cepts to specify such aspects of a capability.Speci?cally, communication requirements are speci?ed as scenes,and the operational description of task-decomposers is speci?ed us-ing performative scenes.

ORCAS integrates the architectural patterns that are the core of the knowledge modelling stance,and the require-ments of an agent-centered approach to cooperative problem solving.The result is a model of the Cooperative Problem Solving process that enables a MAS to conform by to the requirements of every particular instance of a problem.The key of the model is a Team-Design stage,that applies and solves a”bottom-up design problem”in the context of coop-erative MAS.This process determines a possible con?gura-tion of a team in terms of the tasks to be solved,the capabil-ities required,and the domain knowledge available,in such a way that the speci?c requirements of the problem at hand are satis?ed.ORCAS ideas are endorsed by the implemen-tation of an agent infrastructure that has been tested in prac-tice:the ORCAS e-Institution.But rather than being just an application of the e-Institutions formalism,the ORCAS in-frastructure can be seen as a meta institution where dynamic problem-solving institutions are con?gured on-the-?y so as to satisfy stated problem requirements.Some elements from the electronic institution formalism have been adapted and incorporated as components of the ORCAS ACDL.These elements—-scenes and performative structures—are de-?ned for each capability,and are used as building blocks to build a new electronic institution each time a team is formed. That institution con?gured on-the-?y is the shared social layer used by team members to effectively communicate and coordinate during the teamwork.

Some of our proposals are related to recent research on interoperability among heterogeneous agents.The way our approach describes agent capabilities is similar to the LARKS ACDL(?),used in the RETSINA infrastructure(?), but there also notable differences:?rst of all,ORCAS intro-duces domain models as a key element to maximize reuse of capabilities across several application domains;and second, while the RETSINA approach is focused just in the func-tional aspects of a capability(Grosz&Kraus1996),the ORCAS framework covers also the communication and the coordination aspects of teamwork,using concepts from the e-Institution formalism.

We adhere to the view of Internet as an open environment where providers and requesters of capabilities meet and in-teract to solve speci?c problems by using the resources at hand.This view of Internet as a distributed computational platform is in spirit the same of the Semantic Web initia-tive,in particular,our view of agent capabilities is closer to Semantic Web Services frameworks such as the DAML-S ontology(The DAML-S Consortium2001).From the Semantic Web Services paradigm,building an application is basically a process of composing,connecting and veri-fying the properties of web services in a way that resem-bles the compositional approach taken in the ORCAS Team Design stage.There are,however,two outstanding differ-ences between Semantic Web Services and ORCAS.On the one hand,ORCAS agents are autonomous entities that can decide to accept or to refuse a request,while services are reactive,passive entities which are directly invoked by the client;therefore,instead of a centralized composition of ser-vices,we view the composition of capabilities as a negotia-tion process among autonomous agents.On the other hand, our language for describing capabilities is domain indepen-dent,since it is intended to maximize reuse,while existing SWS frameworks ignore this issue,since they are assume to be domain dependent by nature(a Web service is associ-ated to some concrete domain,like the weather of a speci?c country in a weather forecasting service).

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三、教学内容和基本要求 课程教学内容共54学时,对不同的教学内容,其要求如下。 1、船舶类型 了解民用船舶、军用舰船、高速舰船的种类、用途和特征。 2、船体几何要素 了解船舶外形和船体型线力的一般特征,掌握船体主尺度、尺度比和船型系数。 3、船舶浮性 掌握船舶的平衡条件、船舶浮态的概念;掌握船舶重量、重心位置、排水量和浮心位置的计算方法;掌握近似数值计算方法—梯形法和辛普森法。 2

北师大版七年级数学下册三角形难题全解

的度数;

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过F作FM⊥AC并延长MF交BC于N ∴MN//AB ∵FG//BD ∴四边形GBDF为平行四边形 ∴GB=FN ∵AD⊥BC,CE为角平分线 ∴FD=FM 在Rt△AMF和RtNDF中 ∴△AMF≌△NDF ∴AF=FN ∴AE=BG 解法2: 解:作EH⊥BC于H,如图, ∵E是角平分线上的点,EH⊥BC,EA⊥CA, ∴EA=EH, ∵AD为△ABC的高,EC平分∠ACD, ∴∠ADC=90°,∠ACE=∠ECB, ∴∠B=∠DAC, ∵∠AEC=∠B+∠ECB, ∴∠AEC=∠DAC+∠ECA=∠AFE, ∴AE=AF, ∴EG=AF, ∵FG∥BC, ∴∠AGF=∠B, ∵在△AFG和△EHB中, ∠GAF=∠BEH ∠AGF=∠B AF=EH ,∴△AFG≌△EHB(AAS) ∴AG=EB, 即AE+EG=BG+GE, ∴AE=BG. 3、如图,等腰直角三角形ABC中,∠ACB=90°,AD为腰CB上的中线,CE⊥AD交AB 于E.求证∠CDA=∠EDB.

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《船舶原理与结构》课程教学大纲 一、课程基本信息 1、课程代码: 2、课程名称(中/英文):船舶原理与结构/Principles of Naval Architecture and Structures 3、学时/学分:60/3.5 4、先修课程:《高等数学》《大学物理》《理论力学》 5、面向对象:轮机工程、交通运输工程 6、开课院(系)、教研室:船舶海洋与建筑工程学院船舶与海洋工程系 7、教材、教学参考书: 《船舶原理》,刘红编,上海交通大学出版社,2009.11。 《船舶原理》(上、下册),盛振邦、刘应中主编,上海交通大学出版社,2003、2004。 《船舶原理与结构》,陈雪深、裘泳铭、张永编,上海交通大学出版社,1990.6。 《船舶原理》,蒋维清等著,大连海事大学出版社,1998.8。 相关法规和船级社入级规范。 二、课程性质和任务 《船舶原理与结构》是轮机工程和交通运输工程专业的一门必修课。它的主要任务是通过讲课、作业和实验环节,使学生掌握船舶静力学、船舶阻力、船舶推进、船舶操纵性与耐波性和船体结构的基本概念、基本原理、基本计算方法,培养学生分析、解决问题的基本能力,为今后从事工程技术和航运管理工作,打下基础。 本课程各教学环节对人才培养目标的贡献见下表。

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第五章知识结构如下图所示: 第六章知识结构 第七章知识结构框图如下:

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Web of Knowledge平台服务功能使用简介 ISI Web of Knowledge为读者提供了个性化服务和定题服务功能。个性化定制指用户可以在Web of Konwledge主页上注册并设置自己密码,然后每次登录后即可浏览自己订制主页,包括:保存检索策略、建立并编辑自己经常阅读期刊列表;浏览保存检索策略、及时了解一个定题服务是否有效及过期时间。 电子邮件定题服务可让用户方便地跟踪最新研究信息。新定题服务功能允许用户通过web of science中任一种检索途径(普通检索、引文检索、化学结构检索)创建定题服务服务策略,将检索策略保存并转化为用电子邮件通知定题服务。 如图,在Web of Knowledge主页右侧提供了个性化定制服务和定题服务管理功能,下面就这几项功能一一说明如下: 图一:web of knowledge主页 一、注册 点击“Register”超链接,进入注册页面。如图二:分别按要求填入您电子邮件地址,您选择密码以及您姓名。您可以选择自动登录或者普通方式进入您个性化服务管理功能。自动登录可以免除您每次登录Web of Knowledge平台时输入电子邮件地址和密码。该功能使用是cookie技术。如果使用公共计算机,最好选择普通登录方式。 在完成以上操作之后,点击“Submit Registration”完成整个注册过程。

图二:用户注册页面 二、登录 作为注册用户,您可以实现以下功能: ?自动登录到Web of Knowledge平台。 ?选择一个自己常用开始页面,每次登录后则自动进入该页面。 ?将检索策略保存到ISI Web of Knowledge服务器。 ?创建用户关注期刊列表以及期刊目次定题服务。 登录时,在web of knowledge主页右侧输入您电子邮件地址和密码,点击“Sign in”则可进入您个人定制信息服务管理页面。 三、个人信息管理和选择开始页面 点击“My Preferences”超链接,可以编辑个人信息和选择开始页面。 编辑个人信息: 点击“Edit my Information”超链接可以更新您联系信息,如电子邮件地址、密码及姓名。如图三。

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