机器人英文论文及翻译

Behavior Robot

Introduction

As a design strategy, the behavior-based approach has produced intelligent systems for use in

a wide variety of areas, including military applications, mining, space exploration, agriculture,

factory automation, service industries, waste management, health care, disaster intervention

and the home. To understand what behavior-based robotics is, it may be helpful to explain

what it is not. The behavior-based approach does not necessarily seek to produce cognition or

a human-like thinking process. While these aims are admirable, they can be misleading. Blaise

Pascal once pointed out the dangers

inherent when any system tries to model itself. It is natural for humans to

model their own intelligence. The problem is that we are not aware of the

myriad internal processes that actually produce our intelligence, but rather

experience the emergent phenomenon of "thought." In the mid-eighties,

Rodney Brooks (1986) recognized this fundamental problem and

responded with one of the first well-formulated methodologies of the

behavior-based approach. His underlying assertion was that cognition is a

chimera contrived by an observer who is necessarily biased by his/her own

perspective on the environment. (Brooks 1991) As an entirely subjective

fabrication of the observer, cognition cannot be measured or modeled

scientifically. Even researchers who did not believe the phenomenon of

cognition to be entirely illusory, admitted that AI had failed to produce it.

Although many hope for a future when intelligent systems will be able to

model human-like behavior accurately, they insist that this high-level

behavior must be allowed to emerge from layers of control built from the bottom up. While

some skeptics argue that a strict behavioral approach could never scale up to human modes of

intelligence, others argued that the bottom-up behavioral approach is the very principle

underlying all biological intelligence. (Brooks 1990) To many, this theoretical question simply

was not the issue. Instead of focusing on designing systems that could think intelligently, the

emphasis had changed to creating agents that could A Nomad robot used by

many researchers to

study behavior within a

laboratory setting. act intelligently. From an engineering

point of view, this change rejuvenated

robotic design, producing physical robots

that could accomplish real-world tasks

without being told exactly how to do them.

From a scientific point of view,

researchers could now avoid high-level,

armchair discussions about intelligence.

Instead, intelligence could be assessed

more objectively as a measurement of

rational behavior on some task. Since

successful completion of a task was now

the goal, researchers no longer focused

on designing elaborate processing

systems and instead tried to make the

coupling between perception and action as direct as possible. This aim remains the

distinguishing characteristic of behavior-based robotics.

The sub-sections which follow explain the roots of behavior based robotics, how it rose as a

counter to the symbolic, deliberative approach of classical AI and how it has come to be a

standard approach for developing autonomous robots.

A special thanks to Ronald Arkin whose book, Behavior Based Robotics, has greatly

influenced this report.

Understanding the Context of Classical AI

Classical AI spent decades trying to model human-like intelligence, using knowledge-based

systems that processed representation at a high, symbolic level. Symbolic representation was

considered of paramount importance because it allowed agents to operate on sophisticated

human concepts and report on their action at a linguistic level. As Donald Michie stated, "In

AI-type learning, explainability is all." (Michie 1988) Since the goal of early AI was to produce

human-like intelligence, researchers used human-like approaches. Marvin Minsky, in many

ways a father of the field of AI, believed an intelligent machine should, like a human, first build

a model of its environment and then explore solutions abstractly before enacting strategies in

the real world. (McCarthy et al. 1955) This emphasis on symbolic representation and planning

had a great effect on robotics and spurred control strategies where functionality was coded

using languages and programming architectures that made conceptual sense to a human

designer. Although many of the strategies developed were both elaborate and elegant, the

problem was that the intelligence in these systems belonged to the designer. The robot itself

had little or no autonomy and often failed to perform if the environment changed. While