This article argues that autonomy, not problem-solving prowess, is the key property that defines the intuitive notion of "intelligent creature." The presence of these attributes gives autonomous systems the appearance of nondeterminism, but they can all be present in deterministic artifacts and living systems. We argue that autonomy means having the right kinds of goals and the ability to select goals from an existing set, not necessarily creating new goals. We analyze the concept of goals in problem-solving systems in general and establish criteria for the types of goals that characterize autonomy.

This article argues that autonomy, not problem-solving prowess, is the key property that defines the intuitive notion of "intelligent creature." To build an intelligent artificial entity that will act autonomously, we must first understand the attributes of a system that lead us to call it autonomous. The presence of these attributes gives autonomous systems the appearance of nondeterminism, but they can all be present in deterministic artifacts and living systems. We argue that autonomy means having the right kinds of goals and the ability to select goals from an existing set, not necessarily creating new goals. We analyze the concept of goals in problem-solving systems in general and establish criteria for the types of goals that characterize autonomy.

Each is supported and work to find a general solution to that by a formalized theory of the moves in the class of problems.

Fields 3-8 of table 1 of the survey and general results, a discussion represent purposes, specifically, to define of the four hypotheses, and two sections models (field 3), prove theorems about the at the end of the article that contain details of models (field 4), present algorithms (field 5), the survey and statistical analyses. The next analyze algorithms (field 6), present systems section (The Survey) briefly describes the 16 or architectures (field 7), and analyze them substantive questions I asked about each (field 8). These purposes are not mutually paper. One of the closing sections (An Explanation exclusive; for example, many papers that of the Fields in Table 1) discusses the present models also prove theorems about criteria for answering the survey questions the models.

"We describe a robot control architecture which combines a stimulus-response subsystem for rapid reaction, with a search-based planner for handling unanticipated situations. The robot agent continually chooses which action it is to perform, using the stimulusresponse subsystem when possible, and falling back on the planning subsystem when necessary. Whenever it is forced to plan, it applies an explanation-based learning mechanism to formulate a new stimulus-response rule to cover this new situation and others similar to it. With experience, the agent becomes increasingly reactive as its learning component acquires new stimulus-response rules that eliminate the need for planning in similar subsequent situations. This Theo-Agent architecture is described, and results are presented demonstrating its ability to reduce routine reaction time for a simple mobile robot from minutes to under a second."In AAAI-90, Vol. 2, pp. 1051– 1058

Robotics and Autonomous Systems, 6 (1-2), 35-48.

Universal plans address the tension between reasoned behavior and timely response by caching reactions for classes of possible situations. This technique reduces the average time required to select a response at the expense of the space required to store the cache-the classic time-space trade-off. In his article, Matthew Ginsberg argues from the time extreme and against the space extreme. Although I find both extremes undesirable, I defend an increase in space consumption.

Phoenix is a real-time, adaptive planner that manages forest fires in a simulated environment. Alternatively, Phoenix is a search for functional relationships between the designs of agents, their behaviors, and the environments in which they work. In fact, both characterizations are appropriate and together exemplify a research methodology that emphasizes complex, dynamic environments and complete, autonomous agents. This article describes the underlying methodology and illustrates the architecture and behavior of Phoenix agents.

To demonstrate these methods we have trained an ANS network to drive a vehicle through simulated rreeway traffic. I ntJooducticn Computational systems employing fine grained parallelism are revolutionizing the way we approach a number or long standing problems involving pattern recognition and cognitive processing. Thefield spans a wide variety or computational networks, rrom constructs emulating neural runctions, to more crystalline configurations that resemble systolic arrays. Several titles are used to describe this broad area or research, we use the term artificial neural systems (ANS). Our concern inthis work is the use or ANS ror manually training certain types or autonomous systems where the desired rules of behavior are difficult to rormulate. Artificial neural systems consist of a number or processing elements interconnected in a weighted, user-specified fashion, the interconnection weights acting as memory ror the system. Each processing element calculatE', an output value based on the weighted sum or its inputs. In addition, the input data is correlated with the output or desired output (specified by an instructive agent) in a training rule that is used to adjust the interconnection weights.

To demonstrate these methods we have trained an ANS network to drive a vehicle through simulated rreeway traffic. I ntJooducticn Computational systems employing fine grained parallelism are revolutionizing the way we approach a number or long standing problems involving pattern recognition and cognitive processing. The field spans a wide variety or computational networks, rrom constructs emulating neural runctions, to more crystalline configurations that resemble systolic arrays. Several titles are used to describe this broad area or research, we use the term artificial neural systems (ANS). Our concern in this work is the use or ANS ror manually training certain types or autonomous systems where the desired rules of behavior are difficult to rormulate. Artificial neural systems consist of a number or processing elements interconnected in a weighted, user-specified fashion, the interconnection weights acting as memory ror the system. Each processing element calculatE', an output value based on the weighted sum or its inputs. In addition, the input data is correlated with the output or desired output (specified by an instructive agent) in a training rule that is used to adjust the interconnection weights.