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Robert E. Wray and Randolph M. Jones using barcode drawer for visual .net control to generate, create barcode code 128 image in visual .net applications. .NET Framework interested readers m Visual Studio .NET Code128 ay access to learn the details of Soar at the programming level. The Soar architecture grew out of the study of human problem solving.

Soar is often used as a tool for the creation of ne-grained cognitive models that detail and predict aspects of human behavior in the performance of a task. Newell (1990) has taken this effort as far as proposing Soar as a candidate uni ed theory of cognition (UTC) a theory of human cognition that spans and uni es the many observed regularities in human behavior. Evaluating Soar as a UTC remains an active area of work.

An example is Chong s development of a hybrid architecture that incorporates Elements of EPIC, ACT-R, and Soar (EASE) (Chong, 2003). However, Soar is increasingly used as a tool useful for building intelligent agents, especially agents that individually encode signi cant knowledge and capability. Obviously, these agents could behave in ways comparable to humans in particular application domains, but the focus is not limited to human behavior representations.

This chapter therefore describes the general commitments of the Soar architecture as a platform for intelligent systems (human and/or otherwise) and the application of these principles in the development of intelligent, individual and multiagent systems.. soar as a general theory of intelligence As an intelligent ag .NET ANSI/AIM Code 128 ent architecture, the theoretical principles motivating Soar s design are important for two reasons. First, the theory provides insight in understanding Soar as an implementation platform, especially in terms of agent design decisions.

The processes and representations of the Soar architecture are derived directly from the theory. Second, just like any software architecture, Soar biases agent implementations towards particular kinds of solutions. Allen Newell referred to this as listening to the architecture (Newell, 1990).

Understanding the theory makes it easier to understand these biases in approach and implementation. 2.1 The Knowledge Level, Symbol Level, and Architecture An agent can be described at three distinct levels: the knowledge level, the symbol level, and the architecture level (Newell, 1990).

The knowledge level refers to an external, descriptive view of an agent (Newell, 1982). The knowledge level assumes the principle of rationality, which says that if an agent has some knowledge that is relevant to the situation, it will bring it to bear. The knowledge level is a level for analysis; one observes the actions of an agent and makes assumptions about the knowledge it has (and does not) based on the observations.

However, that knowledge must be encoded in some form. Soar assumes knowledge is encoded in a symbol system, which provides the means for universal computation. Considering Soar as an Agent Architecture (Newell, 1980a, 1990 .net vs 2010 Code128 ). The symbol level is the level in which the knowledge of a Soar agent (or any other agent using a symbolic representation) is represented.

Although it is common to think of an agent as having knowledge, in reality every system (human or otherwise) has only a representation of knowledge. The knowledge representations of the symbol level must be accessed, remembered, constructed, acted on, etc. before an observer can ascribe knowledge to the agent.

The xed mechanisms and representations that are used to realize the symbol system comprise the agent architecture. An architecture enables the distinct separation of content (the agent program) from its processing substrate. Thus, the primary difference in Soar applications, from simple expert systems, to natural language interpretation, to real-time models of human behavior, consists of differences in the encoding of knowledge for these applications.

Because Soar (as a symbol system) provides universal computation, it should be suf cient for any application requiring intelligent behavior (assuming intelligence can be captured in computational terms). However, performance ef ciency and the ease with which particular algorithms are encoded and retrieved also have an impact on the suf ciency of the architecture for producing intelligent behavior in a particular application. When researchers discover that Soar is unable to produce some desired behavior or that representation of some behavior is too costly (in terms of performance or solution encoding), a search is begun to extend or change the architecture to address the requirements of the missing capability.

Laird and Rosenbloom (1995) discuss why and how the Soar architecture has evolved since its initial implementation in the early 1980s. Finally, although symbol systems may attempt to approximate it, they will necessarily always fall somewhat short of the perfect rationality of the knowledge level. One can think of the way in which a system falls short of the knowledge level as its particular psychology ; it may not act in time to appear to have the knowledge, it may use some xed process for con ict resolution that leads to a failure to consider some relevant knowledge, etc.

One of the fundamental tensions in the development of Soar has been whether its psychology should be minimized as much as possible, in order to better approximate the knowledge level, or if its limitations (because every symbol level system will have some limitations) should attempt to re ect human limitations. Super cially, a single architecture probably cannot satisfy both constraints. However, one counterargument is that evolution has provided a good approximation of the knowledge level in human symbol processing, and taking advantage of that evolutionary design process, by attempting to replicate it, will result in better symbol systems.

For example, a memory decay mechanism for Soar was resisted for a long time because it appeared to be an artifact of the human symbol system and provided no functional advantage. However, recent.
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