tailieunhanh - Artificial Intelligence - Lecturer 12: Planning

Outline: Planning problem, state-space search, partial-order planning, planning graphs, planning with propositional logic. Planning is the task of determining a sequence of actions that will achieve a goal. Domain independent heuristics and strategies must be based on a domain independent representation. | Artificial Intelligence For HEDSPI Project Lecturer 12 - Planning Lecturers Thanh Huong Duc Khanh Dr. Hai V. Pham HUST 1 Outline Planning problem State-space search Partial-order planning Planning graphs Planning with propositional logic 2 2 1 Search vs. planning Consider the task get milk bananas and a cordless drill Standard search algorithms seem to fail miserably Planning problem Planning is the task of determining a sequence of actions that will achieve a goal. Domain independent heuristics and strategies must be based on a domain independent representation General planning algorithms require a way to represent states actions and goals STRIPS ADL PDDL are languages based on propositional or first-order logic Classical planning environment fully observable deterministic finite static and discrete. 2 Additional complexities Because the world is . Dynamic Stochastic Partially observable And because actions take time have continuous effects AI Planning background Focus on classical planning assume none of the above Deterministic static fully observable Basic Most of the recent progress Ideas often also useful for more complex problems 6

• Kỹ thuật lập trình
• Artificial Intelligence
• Trí tuệ nhân tạo
• Planning problem
• State space search
• Partial order planning
• Planning graphs
• Applying artificial intelligence on e learning
• Compare E learning
• Traditional learning
• Learn 1 1 with virtual assistant
• Artificial Neural Networks (ANN) algorithms
• CS 486686 Artificial Intelligence
• Course administration
• Intelligent Assistive Technology
• A brief history of A
• Properties of task environments
• Artificial intelligence in education
• Keynote speaker abstracts
• Human computer interface issues
• Pedagogical agents
• Models of criminal liability of artificial intelligence
• Criminal liability of artificial intelligence
• Science fiction to prospect for criminal law and policy in Vietnam
• Criminal law and policy in Vietnam
• Criminal law and policy
• Lecture Artificial Intelligence
• Computing machinery
• Thinking humanly
• Cognitive Science
• Lecture note Artificial Intelligence
• Performance measure
• Environment types
• Agent program
• Problem solving
• Problem solving agents
• Problem types
• Problem formulation
• Informed search algorithms
• Best first search
• Local search algorithms
• Simulated annealing
• Genetic algorithms
• Constraint Satisfaction Problems
• Backtracking search for CSPs
• Game playing
• Resource limits
• Games of chance
• Logical agents
• Knowledge based agents
• Wumpus world
• First order logic
• Semantics of FOL
• Fun with sentences
• Wumpus world in FOL
• Inference in first order logic
• Generalized Modus Ponens
• Logic programming
• Bayes’ Rule
• Methods for handling uncertainty
• Making decisions under uncertainty
• Bayesian networks
• Parameterized distributions
• Inference in Bayesian networks
• Variable elimination
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• Hidden Markov models
• Kalman filters
• Speech recognition
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• Rational decisions
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• Multiattribute utilities
• Learning from Observations
• Learning agents
• Inductive learning