5 CONSTRAINT SATISFACTION PROBLEMS
indicating which solutions are preferred. For example, in a university timetabling problem, Prof. X might prefer teaching in the morning whereas Prof. Y prefers teaching in the after-noon. A timetable that has Prof. X teaching at 2 p.m. would still be a solution (unless Prof. X happens to be the department chair), but would not be an optimal one.
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20 STATISTICAL LEARNING METHODS - Artificial intelligence
aima.cs.berkeley.edu20 STATISTICAL LEARNING METHODS In which we view learning as a form of uncertain reasoning from observations. Part V pointed out the prevalence of uncertainty in real environments.
INTRODUCTION
aima.cs.berkeley.eduCHAPTER 3 SOLVING PROBLEMS BY SEARCHING function BEST-FIRST-SEARCH(problem,f) returns a solution node or failure node ←NODE(STATE=problem.INITIAL) frontier ←a priority queue orderedby f, with node as an element reached ←a lookup table, with one entry with key problem.INITIAL and value node while not IS-EMPTY(frontier) do node ←POP(frontier) if …
Artificial Intelligence
aima.cs.berkeley.eduPEARSONSERIES IN ARTIFICIAL INTELLIGENCE Stuart Russell and Peter Norvig, Editors FORSYTH & PONCE Computer Vision: A Modern Approach, 2nd ed. GRAHAM ANSI Common Lisp JURAFSKY & MARTIN Speech and Language Processing, 2nd ed. NEAPOLITAN Learning Bayesian Networks RUSSELL & NORVIG Artificial Intelligence: A Modern Approach, 4th ed.
7 LOGICAL AGENTS
aima.cs.berkeley.eduall the basic concepts of logic. There is also a well-developed technology for reasoning in propositional logic, which we describe in sections 7.5 and 7.6. Finally, Section 7.7 combines the concept of logical agents with the technology of propositional logic to build some simple agents for the wumpus world.
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