Classical Planning

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Restrictive Assumptions [Nau09] Concept of AI Planning Methodology of Planners A0: Finite system finitely many states, actions, events A1: Fully observable the controller always Σ’s current state A2: Deterministic each action has only one outcome A3: Static (no exogenous events) no changes but the controller’s actions A4: Attainment goals existency a set of goal states Sg A5: Sequential plans a plan is a linearly ordered sequence of actions < a0, a1, . . . , an > A6: Implicit time no time durations; linear sequence of instantaneous states A7: Off-line planning planner doesn’t know the execution status Radek Maˇrík (marikr@felk.cvut.cz) Classical Planning 15. dubna 2013 17 / 71
Classical Planning [Nau09] Concept of AI Planning Methodology of Planners Requires all 8 restrictive assumptions Offline generation of action sequences for a deterministic, static, finite system, with complete knowledge, attainment goals, and implicit time. Reduces to the following problem: Given (Σ, s0, Sg ) Find a sequence of actions π =< a0, a1, . . . , an >, that produces a sequence of state transitions < s0, s1, . . . , sn > such that sn ∈ Sg . This is just path-searching in a graph Nodes = states Edges = actions Is this trivial? Radek Maˇrík (marikr@felk.cvut.cz) Classical Planning 15. dubna 2013 18 / 71
Page 1 and 2: Classical Planning Radek Maˇrík C
Page 3 and 4: Concept of Plan [Nau09] Concept of
Page 5 and 6: Concept of AI Planning Definition P
Page 7 and 8: Concept of AI Planning Conceptual M
Page 9 and 10: Concept of AI Planning Conceptual M
Page 11 and 12: Planning Task [Nau09] Concept of AI
Page 13: Types of Planners [Nau09] Domain-sp
Page 17 and 18: Representation STRIPS Classical Rep
Page 19 and 20: Representation STRIPS STRIPS - stat
Page 21 and 22: Representation STRIPS STRIPS - Oper
Page 23 and 24: Representation STRIPS Applicability
Page 25 and 26: Representation STRIPS STRIPS: Plann
Page 27 and 28: Overview of PDDL [Wic11] Representa
Page 29 and 30: Representation PDDL Monkey Planning
Page 31 and 32: Planning Methods Logics and Searchi
Page 33 and 34: Planning Methods Logics and Searchi
Page 35 and 36: Planning Methods State Space State-
Page 37 and 38: Planning Methods State Space Planni
Page 45 and 46: Backward Search [Wic11] 1 Backward-
Page 47 and 48: Planning Methods State Space Sussma
Page 49 and 50: Planning Methods State Space Sussma
Page 51 and 52: Summary Conclusions Summary What is
Page 53 and 54: PDDL Domains [Wic11] Pˇríloha PDD
Page 55 and 56: Pˇríloha PDDL Specification PDDL
Page 57 and 58: PDDL Action [Wic11] Pˇríloha PDDL
Page 59 and 60: PDDL Effects [Wic11] Pˇríloha PDD
Page 61 and 62: PDDL Problem [Wic11] Pˇríloha PDD
Page 63: References I Dana Nau. CMSC 722, ai

Classical Planning

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