Intro to Logic. Propositional logic. Inference Rules. First-order Logic. Logic Proofs & Deduction. Use re-write rules to “deduce” from what is known to what is unknown. These rules can be quite complex. This idea can be used to create Automatic Theorem Provers . Limitations.By indiya
CS10 The Beauty and Joy of Computing Lecture #16 : Computational Game Theory 2011-10-26. UC Berkeley EECS Lecturer SOE Dan Garcia.By denver
IE 607 Heuristic Optimization Introduction to Optimization. Objective Function Max (Min) some function of decision variables Subject to (s.t.) equality (=) constraints inequality ( ) constraints Search SpaceBy theodore-pallas
IE 607 Heuristic Optimization Introduction to Optimization. Objective Function Max (Min) some function of decision variables Subject to (s.t.) equality (=) constraints inequality ( ) constraints Search SpaceBy jimenezjohn
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Structures and Strategies for State Space Search. IntroductionGraph TheoryStructures for state space searchstate space representation of problemsStrategies for state space searchData-Driven and Goal-Driven SearchImplementing Graph SearchDepth-First and Breadth-First SearchDepth-First Search with Iterative DeepeningUsing the State Space to Represent Reasoning with the Predicate CalculusState Space Description of a Logical SystemAND/OR graphsExamples and Applications.
State Space Search. State Space representation of a problem is a graph. Nodes correspond to problem states Arcs correspond to steps in a solution process One node corresponds to an initial state One node corresponds to a goal state. Solution Path .
State Space Search. Classic AI. State Space representation of a problem is a graph. Nodes correspond to problem states Arcs correspond to steps in a solution process One node corresponds to an initial state One node corresponds to a goal state. Solution Path .
State Space Search. Nattee Niparnan. Optimization Example: Finding Max Value in an Array. There are N possible answers The first element The second element 3 rd , 4 th … Try all of them Remember the best one. State Space Search Framework. VERY IMPORTANT!!.
State Space Search. Backtracking. Suppose We are searching depth-first No further progress is possible (i.e., we can only generate nodes we’ve already generated), then backtrack. The algorithm: First Pass. Pursue path until goal is reached or dead end If goal, quit and return the path
State-Space Search. Artificial Intelligence Programming in Prolog Lecturer: Tim Smith Lecture 8 18/10/04. State-Space Search. Many problems in AI take the form of state-space search .
State Space Search:. Breadth First and Depth First. Motivations. Many problems can be viewed as reaching a goal state from a given starting point, e.g., the farmer-wolf-goat-cabbage problem. Often there is an underlying state space successor function to proceed from one state to the next.
State-Space Search. Computers should solve problems. A traditional problem domain for AI is the blocks world . Problem description: Given blocks A, B and C, with C on B, arrange the blocks so that A is on B and B is on C. You may only pick up and move one block at a time.
State Space Search. Optimization Problem. Problem. Problem. Description of valid input Description of desired output. Instance 1. Instance 2. …. Instance 3. Instance N. Algorithm. Instance X. Algorithm. Solution for X. Optimization Problem.
State-Space Search. Outline: Demonstration with T* State spaces, operators, moves A Puzzle: The “Painted Squares” Combinatorics of the Puzzle Representations for pieces, boards, and states. Recursive depth-first search Iterative depth-first search. Introductory Demo.