Automated (AI) Planning Planning by state-space search Automated (AI) Planning Progression Planning tasks & Search Regression Search algorithms for planning Uninformed search Carmel Domshlak Heuristic search State-space search Automated (AI) Planning Planning by state-space search: one of the big success stories of AI state-space search many planning algorithms based on state-space search Introduction (we'll see some other algorithms later, though) Classification Progression will be the focus of this and the following topics Regression we assume prior knowledge of basic search algorithms Search algorithms for uninformed vs. informed planning systematic vs. local Uninformed search background on search: Russell & Norvig, Artificial Heuristic Intelligence { A Modern Approach, chapters 3 and 4 search Satisficing or optimal planning? Automated (AI) Planning Must carefully distinguish two different problems: Planning by satisficing planning: any solution is OK state-space search (although shorter solutions typically preferred) Introduction optimal planning: plans must have shortest possible length Classification Progression Regression Both are often solved by search, but: Search algorithms for details are very different planning Uninformed almost no overlap between good techniques for satisficing search planning and good techniques for optimal planning Heuristic search many problems that are trivial for satisficing planners are impossibly hard for optimal planners Planning by state-space search Automated (AI) Planning Planning by How to apply search to planning? many choices to make! state-space search Introduction Choice 1: Search direction Classification progression: forward from initial state to goal Progression Regression regression: backward from goal states to initial state Search algorithms for bidirectional search planning Uninformed search Heuristic search Planning by state-space search Automated (AI) Planning Planning by How to apply search to planning? many choices to make! state-space search Introduction Choice 2: Search space representation Classification search nodes are associated with states Progression Regression search nodes are associated with sets of states Search algorithms for planning Uninformed search Heuristic search Planning by state-space search Automated (AI) Planning Planning by How to apply search to planning? many choices to make! state-space search Introduction Choice 3: Search algorithm Classification uninformed search: Progression depth-first, breadth-first, iterative depth-first, . Regression Search algorithms for heuristic search (systematic): planning ∗ ∗ ∗ greedy best-first, A , Weighted A , IDA ,... Uninformed search heuristic search (local): Heuristic hill-climbing, simulated annealing, beam search, . search Planning by state-space search Automated (AI) Planning Planning by How to apply search to planning? many choices to make! state-space search Introduction Choice 4: Search control Classification heuristics for informed search algorithms Progression Regression pruning techniques: invariants, symmetry elimination, Search helpful actions pruning, . algorithms for planning Uninformed search Heuristic search Search-based satisficing planners Automated (AI) Planning Planning by state-space FF (Hoffmann & Nebel, 2001) search Introduction search direction: forward search Classification search space representation: single states Progression Regression search algorithm: enforced hill-climbing (informed local) Search algorithms for heuristic: FF heuristic (inadmissible) planning pruning technique: helpful actions (incomplete) Uninformed search Heuristic one of the best satisficing planners search Search-based optimal planners Automated (AI) Planning Planning by HHH state-space Fast Downward + h (Helmert, Haslum & Hoffmann, 2007) search Introduction search direction: forward search Classification search space representation: single states Progression Regression ∗ search algorithm:A (informed systematic) Search algorithms for heuristic: merge-and-shrink abstractions (admissible) planning Uninformed pruning technique: none search Heuristic one of the best optimal planners search Our plan for the next lectures Automated (AI) Planning Choices to make: Planning by state-space 1 search direction: progression/regression/both search Introduction Classification this chapter Progression 2 search space representation: states/sets of states Regression this chapter Search algorithms for 3 search algorithm: uninformed/heuristic; systematic/local planning Uninformed this chapter search 4 search control: heuristics, pruning techniques Heuristic following chapters search Planning by forward search: progression Automated (AI) Planning Planning by Progression: Computing the successor state appo(s) of a state state-space s with respect to an operator o. search Progression Progression planners find solutions by forward search: Overview Example start from initial state Regression Search iteratively pick a previously generated state and progress it algorithms for through an operator, generating a new state planning Uninformed solution found when a goal state generated search Heuristic pro: very easy and efficient to implement search Search space representation in progression planners Automated (AI) Planning Two alternative search spaces for progression planners: Planning by 1 search nodes correspond to states state-space search when the same state is generated along different paths, Progression it is not considered again (duplicate detection) Overview pro: fast Example con: memory intensive (must maintain closed list) Regression Search 2 search nodes correspond to operator sequences algorithms for planning different operator sequences may lead to identical states Uninformed (transpositions) search pro: can be very memory-efficient Heuristic con: much wasted work (often exponentially slower) search first alternative usually preferable Progression planning example (depth-first search) Automated (AI) Planning I Planning by state-space search Progression Overview Example Regression Search algorithms for planning Uninformed search Heuristic G search Progression planning example (depth-first search) Automated (AI) Planning I Planning by state-space search Progression Overview Example Regression Search algorithms for planning Uninformed search Heuristic G search Progression planning example (depth-first search) Automated (AI) Planning I Planning by state-space search Progression Overview Example Regression Search algorithms for planning Uninformed search Heuristic G search Progression planning example (depth-first search) Automated (AI) Planning I Planning by state-space search Progression Overview Example Regression Search algorithms for planning Uninformed search Heuristic G search Progression planning example (depth-first search) Automated (AI) Planning I Planning by state-space search Progression Overview Example Regression Search algorithms for planning Uninformed search Heuristic G search Progression planning example (depth-first search) Automated (AI) Planning I Planning by state-space search Progression Overview Example Regression Search algorithms for planning Uninformed search Heuristic G search Progression planning example (depth-first search) Automated (AI) Planning I Planning by state-space search Progression Overview Example Regression Search algorithms for planning Uninformed search Heuristic G search Progression planning example (depth-first search) Automated (AI) Planning I Planning by state-space search Progression Overview Example Regression Search algorithms for planning Uninformed search Heuristic G search Progression planning example (depth-first search) Automated (AI) Planning I Planning by state-space search Progression Overview Example Regression Search algorithms for planning Uninformed search Heuristic G search Progression planning example (depth-first search) Automated (AI) Planning I Planning by state-space search Progression Overview Example Regression Search algorithms for planning Uninformed search Heuristic G search Forward search vs. backward search Automated (AI) Planning Going through a transition graph in forward and backward Planning by state-space directions is not symmetric: search forward search starts from a single initial state; Progression backward search starts from a set of goal states Regression Overview Example when applying an operator o in a state s in forward STRIPS 0 direction, there is a unique successor state s ; Search 0 algorithms for if we applied operator o to end up in state s , planning there can be several possible predecessor states s Uninformed search most natural representation for backward search in planning Heuristic associates sets of states with search nodes search Planning by backward search: regression Automated (AI) Planning Regression: Computing the possible predecessor states regro(S) of a set of states S with respect to the last operator o that was Planning by state-space applied. search Regression planners find solutions by backward search: Progression Regression start from set of goal states Overview Example iteratively pick a previously generated state set and STRIPS Search regress it through an operator, generating a new state set algorithms for planning solution found when a generated state set includes the Uninformed initial state search Heuristic search Pro: can handle many states simultaneously Con: basic operations complicated and expensive Search space representation in regression planners Automated (AI) Planning Planning by state-space identify state sets with logical formulae: search