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Symbol Level
In knowledge-based systems, agents choose actions based on the principle of rationality to move closer to a desired goal. The agent is able to make decisions based on knowledge it has about the world (see knowledge level). But for the agent to actually change its state, it must use whatever means it has available. This level of description for the agent's behavior is the symbol level. The term was coined by Allen Newell in 1982. For example, in a computer program, the knowledge level consists of the information contained in its data structures that it uses to perform certain actions. The symbol level consists of the program's algorithms, the data structures themselves, and so on. See also *Knowledge level modeling Knowledge level modeling is the process of theorizing over observations about a world and, to some extent, explaining the behavior of an agent as it interacts with its environment. Crucial to the understanding of knowledge level modeling are Allen ... References { ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge-based Systems
A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. The term is broad and refers to many different kinds of systems. The one common theme that unites all knowledge based systems is an attempt to represent knowledge explicitly and a reasoning system that allows it to derive new knowledge. Thus, a knowledge-based system has two distinguishing features: a knowledge base and an inference engine. The first part, the knowledge base, represents facts about the world, often in some form of subsumption ontology (rather than implicitly embedded in procedural code, in the way a conventional computer program does). Other common approaches in addition to a subsumption ontology include frames, conceptual graphs, and logical assertions. The second part, the inference engine, allows new knowledge to be inferred. Most commonly, it can take the form of IF-THEN rules coupled with forward chaining or backward chaining appro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Principle Of Rationality
The principle of rationality (or rationality principle) was coined by Karl R. Popper in his Harvard Lecture of 1963, and published in his book ''Myth of Framework''. It is related to what he called the 'logic of the situation' in an ''Economica'' article of 1944/1945, published later in his book ''The Poverty of Historicism''. According to Popper’s rationality principle, agents act in the most adequate way according to the objective situation. It is an idealized conception of human behavior which he used to drive his model of situational analysis. Popper Popper called for social science to be grounded in what he called situational analysis. This requires building models of social situations which include individual actors and their relationship to social institutions, e.g. markets, legal codes, bureaucracies, etc. These models attribute certain aims and information to the actors. This forms the 'logic of the situation', the result of reconstructing meticulously all circumstances ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Level
In artificial intelligence, knowledge-based agents draw on a pool of logical sentences to infer conclusions about the world. At the knowledge level, we only need to specify what the agent knows and what its goals are; a logical abstraction separate from details of implementation. This notion of knowledge level was first introduced by Allen Newell in the 1980s, to have a way to rationalize an agent's behavior. The agent takes actions based on knowledge it possesses, in an attempt to reach specific goals. It chooses actions according to the principle of rationality. Beneath the knowledge level resides the symbol level. Whereas the knowledge level is ''world'' oriented, namely that it concerns the environment in which the agent operates, the symbol level is ''system'' oriented, in that it includes the mechanisms the agent has available to operate. The knowledge level ''rationalizes'' the agent's behavior, while the symbol level ''mechanizes'' the agent's behavior. For example, i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Allen Newell
Allen Newell (March 19, 1927 – July 19, 1992) was a researcher in computer science and cognitive psychology at the RAND Corporation and at Carnegie Mellon University’s School of Computer Science, Tepper School of Business, and Department of Psychology. He contributed to the Information Processing Language (1956) and two of the earliest AI programs, the Logic Theory Machine (1956) and the General Problem Solver (1957) (with Herbert A. Simon). He was awarded the ACM's A.M. Turing Award along with Herbert A. Simon in 1975 for their basic contributions to artificial intelligence and the psychology of human cognition. Early studies Newell completed his Bachelor's degree in physics from Stanford in 1949. He was a graduate student at Princeton University from 1949–1950, where he did mathematics. Due to his early exposure to an unknown field known as game theory and the experiences from the study of mathematics, he was convinced that he would prefer a combination of ex ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Level Modeling
Knowledge level modeling is the process of theorizing over observations about a world and, to some extent, explaining the behavior of an agent as it interacts with its environment. Crucial to the understanding of knowledge level modeling are Allen Newell's notions of the knowledge level, ''operators'', and an agent's ''goal state''. *The ''knowledge level'' refers to the knowledge an agent has about its world. *''Operators'' are what can be applied to an agent to affect its state. *An agent's ''goal state'' is the status reached after the appropriate operators have been applied to transition from a previous, non-goal state. Essentially, knowledge level modeling involves evaluating an agent's knowledge of the world and all possible states and with that information constructing a model that depicts the interrelations and pathways between the various states. With this model, various problem solving methods (i.e. prediction, classification, explanation, tutoring, qualitative reasonin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |