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Mid-term Review Chapters 2-7
Mid-term Review Chapters 2-7. Review Agents (2.1-2.3) Review State Space Search Problem Formulation (3.1, 3.3) Blind (Uninformed) Search (3.4) Heuristic Search (3.5) Local Search (4.1, 4.2) Review Adversarial (Game) Search (5.1-5.4) Review Constraint Satisfaction (6.1-6.4)
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State Space
State Space. Figure out how state space works in general (maybe a bit abstract). Revisit a number of the problems and examples we’ve already seen. Den Hartog problem 17. Exam question 4. The general 1 DOF, forced mass-spring-damper problem. (The automobile suspension system).
State Space
State Space. Heuristic Search. Three Algorithms. Backtrack Depth First Breadth First All work if we have well-defined: Goal state Start state State transition rules But could take a long time. Heuristic. An informed guess that guides search through a state space
State space Initial state
Adversarial Search and Game Playing (Where making good decisions requires respecting your opponent) R&N: Chap. 6. Games like Chess or Go are compact settings that mimic the uncertainty of interacting with the natural world For centuries humans have used them to exert their intelligence
SPACE REVIEW
SPACE REVIEW. Rules. A group will pick a question and have 30 seconds to answer. If they do not get the answer correct, the next group may steal the question. If a group steals a question, it is still their turn to choose the next question.
State-Space Representation
State-Space Representation. Read Chapter 3. Searches you use. MapQuest road maps Google documents CiteSeer research documents. Abstract Model. Initial State Operators: maps a state into a next state alternative: successors of state Goal Predicate: test to see if goal achieved
State Space Search
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 Analysis
State Space Analysis. UNIT-V. outline. How to find mathematical model, called a state-space representation, for a linear, time-invariant system How to convert between transfer function and state space models. State-Space Modeling. Alternative method of modeling a system than
State Space Search
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 Planning
State-Space Planning. Dr. H é ctor Mu ñ oz-Avila. Sources: Ch. 3 Appendix A Slides from Dana Nau’s lecture. Reminder: Some Graph Search Algorithms (I). 20. G = (V,E). G. Z. 10. 16. Edges : set of pairs of vertices (v,v’). 9. B. 6. F. C. 8. 24. 4. A. 6. E. Vertices. D.
State Space Search
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 .