Artificial Intelligence

Transcription

1 Artificial Intelligence Instructor: Tsung-Che Chiang Department of Computer Science and Information Engineering National Taiwan Normal University Texts 2

2 Grading Policy In-class exercises & take-home assignments (65% ~ 85%) C/C++ programming skill is required. There will be at least 4 take-home assignments. Late submissions: ~3 days with 0% penalty, 4~7 days with 20% penalty The submission with 8-day or longer delay will not be accepted. Final exam (20 ~ 0%) Class participation (5%) 3 Syllabus Introduction to Intelligent Agents Search Blind Search Informed Search Constraint Satisfaction Problem Adversarial Search Logic Propositional Logic Soft Computing Fuzzy Systems Artificial Neural Network Metaheuristics 4 2

3 Agents An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment thorough actuators. Artificial Intelligence: A Modern Approach, 2nd ed., Figure Agents What is a rational agent? Task environments (problems) Definition Performance, Environment, Actuator, Sensor Properties Observable, Deterministic, Static, etc. Agent structure Simply reflex Model-based Goal-based Utility-based 6 3

4 Agents Utility-based agent Artificial Intelligence: A Modern Approach, 2nd ed., Figure Homework Best Cleaner Thanks to Mr. Shi-Yau Yu for the interface. 8 4

5 Problem Solving Artificial Intelligence: A Modern Approach, 2nd ed., Figure 3. 9 Problem Solving Example problems Vacuum world 8-puzzle 8-queens 0 5

6 Uninformed Search Strategies Breadth-first search Uniform-cost search Depth-first search Iterative deepening search Bidirectional search 972 Homework 2 Missionaries and Cannibals Problem 2 6

7 Informed Search Strategies Greedy best-first search A* search Memory-bounded heuristic search Artificial Intelligence: A Modern Approach, 2nd ed., Figure 4.3 & Informed Search Strategies Hill climbing Online search (972 Homework 3) 4 7

8 Constraint Satisfaction Problem Backtracking search Variable & value ordering Constraint propagation Intelligent backtracking Local search Problem structure Homework

9 Adversarial Search Artificial Intelligence: A Modern Approach, 2nd ed., Figure 6. 7 Adversarial Search Minimax algorithm Alpha-beta pruning Imperfect, real-time decisions Evaluation function & cut-off test Games including an element of chance 8 9

10 Logical Agents Artificial Intelligence: A Modern Approach, 2nd ed., Figure 7. & Propositional Logic Sentence AtomicSentence ComplexSentence AtomicSentence True False Symbol Symbol P Q R ComplexSentence Sentence (Sentence Sentence) (Sentence Sentence) (Sentence Sentence) (Sentence Sentence) 20 0

11 Propositional Logic Reasoning Modus Ponens And-Elimination Resolution Forward/Backward chaining Backtracking Local search 2 Fuzzy Systems (a) Boolean Logic. (b) Multi-valued Logic Name Chris Mark John Tom David Mike Bob Steven Bill Peter Height, cm Degree of Membership Crisp Fuzzy Degree of Membership Degree of Membership Crisp Sets Fuzzy Sets Tall Men Height, cm Height, cm Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Figure 4. & 4.2, Table 4. 22

12 Fuzzy Systems Degree of Membership Degree of Membership Short Crisp Sets Short Average Short Tall Tall Men Average Fuzzy Sets Height, cm Tall Tall Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Figure Fuzzy Systems Rule : IF Distance is Short AND Health is Good THEN Action is Chasing Rule 2: IF Distance is Long AND Health is Good THEN Action is Do Nothing Rule 3: IF Distance is Short AND Health is Bad THEN Action is Escaping 24 2

13 Fuzzy Inference Mamdani Sugeno Crisp Input x A A2 A3 x (x = A) = 0.5 (x = A2) = 0.2 X B Crisp Input y y (y = B) = 0. (y = B2) = 0.7 B2 Y Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Figure 4.0 A 3 B C O R 0. 0 x X 0 y Y (m a x ) 0 Rule : IF x is A 3 (0.0 ) O R y is B (0.) T H E N z is C (0.) A 2 0 x X A B 2 0 y Y A N D (m in ) Rule 2: IF x is A 2 (0.2 ) A N D y is B 2 (0.7 ) T H E N z is C 2 (0.2 ) C C C C 2 C 2 C 3 Z C 3 Z C 3 0 x X 0 Rule 3: IF x is A (0.5 ) T H E N z is C 3 (0.5 ) Z 25 Artificial Neural Networks Synapse Biological NeuralNetwork Soma Dendrite Axon Synapse Artificial Neural Network Neuron Input Output Weight Dendrites Axon Soma Synapse Dendrites Axon Soma Synapse Inputs x x 2 w w 2 Linear Combiner Hard Limiter Threshold Output Y I n p u t S i g n a l s Input Layer Middle Layer Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Figure 6., 6.2, 6.5 Output Layer O u t p u t S i g n a l s 26 3

14 Artificial Neural Networks Perceptron Back-propagation network Hopfield network Bidirectional associative memory Self-organizing map 27 Metaheuristics Evolutionary computation Ant colony optimization Particle swarm optimization Tabu search 28 4

15 Genetic Algorithms Initial Population Evaluation next generation generation evaluation environmental selection generation 2 Mating selection evaluation mating selection reproduction Reproduction offspring Evaluation Environmental selection N Stop? Y Final Population 29 5