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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 reasoni ... [...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 exper ... [...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, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cognitive Architectures
A cognitive architecture refers to both a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science. The formalized models can be used to further refine a comprehensive theory of cognition and as a useful artificial intelligence program. Successful cognitive architectures include ACT-R (Adaptive Control of Thought - Rational) and SOAR. The research on cognitive architectures as software instantiation of cognitive theories was initiated by Allen Newell in 1990. The Institute for Creative Technologies defines cognitive architecture as: "''hypothesis about the fixed structures that provide a mind, whether in natural or artificial systems, and how they work together – in conjunction with knowledge and skills embodied within the architecture – to yield intelligent behavior in a diversity of complex environments." History Herbert A. Simon, one of the f ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Abductive Reasoning
Abductive reasoning (also called abduction,For example: abductive inference, or retroduction) is a form of logical inference formulated and advanced by American philosopher Charles Sanders Peirce beginning in the last third of the 19th century. It starts with an observation or set of observations and then seeks the simplest and most likely conclusion from the observations. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it. Abductive conclusions are thus qualified as having a remnant of uncertainty or doubt, which is expressed in retreat terms such as "best available" or "most likely". One can understand abductive reasoning as inference to the best explanation, although not all usages of the terms ''abduction'' and ''inference to the best explanation'' are exactly equivalent. In the 1990s, as computing power grew, the fields of law, computer science, and artificial intelligence researchFor examples, seeAbductive Inference i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Logical Reasoning
Two kinds of logical reasoning are often distinguished in addition to formal deduction: induction and abduction. Given a precondition or ''premise'', a conclusion or ''logical consequence'' and a rule or ''material conditional'' that implies the ''conclusion'' given the ''precondition'', one can explain the following. # Deductive reasoning determines whether the truth of a ''conclusion'' can be determined for that ''rule'', based solely on the truth of the premises. Example: "When it rains, things outside get wet. The grass is outside, therefore: when it rains, the grass gets wet." Mathematical logic and philosophical logic are commonly associated with this type of reasoning. # Inductive reasoning attempts to support a determination of the ''rule''. It hypothesizes a ''rule'' after numerous examples are taken to be a ''conclusion'' that follows from a ''precondition'' in terms of such a ''rule''. Example: "The grass got wet numerous times when it rained, therefore: the grass alway ... [...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, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Engineering
Knowledge engineering (KE) refers to all technical, scientific and social aspects involved in building, maintaining and using knowledge-based systems. Background Expert systems One of the first examples of an expert system was MYCIN, an application to perform medical diagnosis. In the MYCIN example, the domain experts were medical doctors and the knowledge represented was their expertise in diagnosis. Expert systems were first developed in artificial intelligence laboratories as an attempt to understand complex human decision making. Based on positive results from these initial prototypes, the technology was adopted by the US business community (and later worldwide) in the 1980s. The Stanford heuristic programming projects led by Edward Feigenbaum was one of the leaders in defining and developing the first expert systems. History In the earliest days of expert systems there was little or no formal process for the creation of the software. Researchers just sat down with dom ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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