人工智能与知识工程【英文】
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N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Genetic Algorithms and Evolutionary Programming
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Introduction to the AI Paradigms
AI objectives: to develop methods and systems for solving problems, usually solved through intellectual activity of humans, eg. image recognition language and speech processing; planning, prediction, etc., thus enhancing the computer information systems to improve our understanding on how the human brain works
An Introduction to Artificial Intelligence and Knowledge Engineering
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Introduction to the AI Paradigms (cont)
AI directions: developing methods and systems for solving AI problems without following the way the humans do (expert systems) developing methods and systems for solving AI problems through modelling the human way of thinking, or the way the brain works (neural networks) AI paradigms: symbolic or sub-symbolic (connectionist)
Sub-topics:
Introduction to the AI paradigms (1.1; pp. 1-3) Heuristic problem solving (1.2; pp. 3-9) Genetic algorithms and evolutionary programming (1.2.3; pp. 9-14) Expert systems (1.3.1; pp. 14-15) Fuzzy systems (1.3.2; pp. 15-17) Neural networks (1.3.3; pp. 17-19) Hybrid systems (1.3.4; 1.9, pp. 65-68)
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Heuristic Problem Solving (cont)
பைடு நூலகம்
Figure 1.2: (a) Ill-informed and (b) well-informed heuristics. They are represented as `patches' in the problem space. The patches have different forms (usually quadrilateral) depending on the way of representing the heuristics in a computer program.
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Heuristic Problem Solving (cont)
Figure 1.3: The problem knowledge maps the domain space into the solution space and approximates the objective (goal) function: (a) a general case; (b) two dimensional case.
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Heuristic Problem Solving
Figure 1.1 Heuristics as means of obtaining restricted projections from the domain space D into the solution space S.
Genetic Algorithms and Evolutionary Programming
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Introduction to the AI Paradigms
AI objectives: to develop methods and systems for solving problems, usually solved through intellectual activity of humans, eg. image recognition language and speech processing; planning, prediction, etc., thus enhancing the computer information systems to improve our understanding on how the human brain works
An Introduction to Artificial Intelligence and Knowledge Engineering
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Introduction to the AI Paradigms (cont)
AI directions: developing methods and systems for solving AI problems without following the way the humans do (expert systems) developing methods and systems for solving AI problems through modelling the human way of thinking, or the way the brain works (neural networks) AI paradigms: symbolic or sub-symbolic (connectionist)
Sub-topics:
Introduction to the AI paradigms (1.1; pp. 1-3) Heuristic problem solving (1.2; pp. 3-9) Genetic algorithms and evolutionary programming (1.2.3; pp. 9-14) Expert systems (1.3.1; pp. 14-15) Fuzzy systems (1.3.2; pp. 15-17) Neural networks (1.3.3; pp. 17-19) Hybrid systems (1.3.4; 1.9, pp. 65-68)
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Heuristic Problem Solving (cont)
பைடு நூலகம்
Figure 1.2: (a) Ill-informed and (b) well-informed heuristics. They are represented as `patches' in the problem space. The patches have different forms (usually quadrilateral) depending on the way of representing the heuristics in a computer program.
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Heuristic Problem Solving (cont)
Figure 1.3: The problem knowledge maps the domain space into the solution space and approximates the objective (goal) function: (a) a general case; (b) two dimensional case.
N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996
Heuristic Problem Solving
Figure 1.1 Heuristics as means of obtaining restricted projections from the domain space D into the solution space S.