AI_Session 28 planning graph.pptx
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This document summarizes session 28 of an Artificial Intelligence course which covered classical planning approaches and planning graphs. It defines planning graphs as a way to approximate the complete search tree of all possible actions and results. Planning graphs work for propositional planning problems and are organized into levels with nodes representing fluents and applicable actions. The document provides details on how to construct a planning graph by propositionalizing actions, adding levels for states and actions, and adding links between and within levels to represent mutex relationships. The next session will cover the GRAPH-PLAN algorithm.
AI_Session 28 planning graph.pptx
- 1. ARTIFICAL INTELLIGENCE (R18 III(II Sem)) Department of computer science and engineering (AI/ML) Session 28 by Asst.Prof.M.Gokilavani VITS 6/11/2023 Department of CSE (AI/ML) 1
- 2. TEXTBOOK: • Artificial Intelligence A modern Approach, Third Edition, Stuart Russell and Peter Norvig, Pearson Education. REFERENCES: • Artificial Intelligence, 3rd Edn, E. Rich and K.Knight (TMH). • Artificial Intelligence, 3rd Edn, Patrick Henny Winston, Pearson Education. • Artificial Intelligence, Shivani Goel, Pearson Education. • Artificial Intelligence and Expert Systems- Patterson, Pearson Education. 6/11/2023 Department of CSE (AI/ML) 2
- 3. Topics covered in session 28 6/11/2023 Department of CSE (AI/ML) 3 Planning Classical Planning: Definition of Classical Planning, Algorithms for Planning with State-Space Search, Planning Graphs, other Classical Planning Approaches, Analysis of Planning approaches. Planning and Acting in the Real World: Time, Schedules, and Resources, Hierarchical Planning, Planning and Acting in Nondeterministic Domains, Multi agent Planning.
- 4. Planning graphs • Graph Plan is an algorithm based on planning graph • Planning graphs are also used as a source of heuristics (an estimate of how many steps it takes to reach the goal) • Planning graph is an approximation of a complete tree of all possible actions and their results Planning graphs work only for propositional planning problems—ones with no variables. 6/11/2023 Department of CSE (AI/ML) 4
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- 7. Planning graphs Planning graph is organized into levels. • Level S0: initial state, consisting of nodes representing each fluent that holds in S0. • Level A0: each ground action that might be applicable in S0 Then alternate Si and Ai. • Si contains fluent which could hold at time i, (may be both P and ¬P); literals may show up too early but never too late. • Ai contains actions which could have their preconditions satisfied at i. 6/11/2023 Department of CSE (AI/ML) 7
- 8. Mutex • A mutex between two actions indicates that it is impossible to perform these actions in parallel. • A mutex between two literals indicates that it is impossible to have these both literals true at this stage. 6/11/2023 Department of CSE (AI/ML) 8
- 9. Example 6/11/2023 9 Department of CSE (AI/ML)
- 10. Constructing a planning graph • All action schemas must be propositionalized • Generate all possible grounded actions so no variables are left. • Start with S0 = all initially true fluents • Construct Ai = all actions whose preconditions are satisfied by Si • Construct Si = all fluents made true by the effects of the actions in Ai-1 • All levels in Ai have the NO-OP action which passes all true fluents in Si to Si+1. 6/11/2023 10 Department of CSE (AI/ML)
- 11. Add links: • Between levels – From fluents in Si to preconditions of actions in Ai – From effects of actions in Ai to fluents in Si+1 • Within Levels (mutual exclusion) – Action Mutex Link: Two actions compete for resources – State Mutex Link: Two fluents that cannot both be true at the same time 6/11/2023 11 Department of CSE (AI/ML)
- 12. Rules for Constructing Mutex Links Action Mutex: • Inconsistent Effects: One action negates the effect of the other (e.g., Eat(Cake) and Bake(Cake) ) • Interference: One of the effects of one action is the negation of a precondition of the other (e.g., Eat(Cake) and the persistence of Have(Cake)) • Competing Needs: One of the preconditions of one action is mutually exclusive with a precondition of the other • Depends on State Mutex links in previous level State Mutex: • One is the negation of the other. • All pairs of actions that could make both true have Act on mutex links between them. 6/11/2023 12 Department of CSE (AI/ML)
- 13. Example 6/11/2023 Department of CSE (AI/ML) 13
- 14. Topics to be covered in next session 29 • GRAPH-PLAN Algorithm 6/11/2023 Department of CSE (AI/ML) 14 Thank you!!!