CSC2542 Introduction to Planning Sheila McIlraith Department of - PowerPoint PPT Presentation

CSC2542 Introduction to Planning Sheila McIlraith Department of Computer Science University of Toronto Fall 2010 1

Acknowledgements Some of the slides used in this course are modifications of Dana Nau’s lecture slides for the textbook Automated Planning, licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License: http://creativecommons.org/licenses/by-nc-sa/2.0/ Other slides are modifications of slides developed by Malte Helmert, Bernhard Nebel, and Jussi Rintanen. I have also used some material prepared by P@trick Haslum. I would like to gratefully acknowledge the contributions of these researchers, and thank them for generously permitting me to use aspects of their presentation material. 2

plan n. 4. A drawing or diagram made to scale showing the structure or arrangement 1. A scheme, program, or method of something. worked out beforehand for the accomplishment of an objective: a 5. In perspective rendering, one of plan of attack. several imaginary planes perpendicular to the line of vision 2. A proposed or tentative project or between the viewer and the object course of action: had no plans for the being depicted. evening. 6. A program or policy stipulating a 3. A systematic arrangement of elements service or benefit: a pension plan. or important parts; a configuration or outline: a seating plan; the plan of a Synonyms: blueprint, design, project, story. scheme, strategy 3

02 Clamp board 03 Establish datum point at bullseye (0.25, 1.00) 004 B VMC1 0.10 0.34 01 Install 0.15-diameter side-milling tool [a representation] of future behavior … 02 Rough side-mill pocket at (-0.25, 1.25) length 0.40, width 0.30, depth 0.50 usually a set of actions, with temporal and 03 Finish side-mill pocket at (-0.25, 1.25) other constraints on them, for execution by length 0.40, width 0.30, depth 0.50 04 Rough side-mill pocket at (-0.25, 3.00) some agent or agents. length 0.40, width 0.30, depth 0.50 - Austin Tate 05 Finish side-mill pocket at (-0.25, 3.00) length 0.40, width 0.30, depth 0.50 [ MIT Encyclopedia of the Cognitive Sciences , 1999] 004 C VMC1 0.10 1.54 01 Install 0.08-diameter end-milling tool [...] 004 T VMC1 2.50 4.87 01 Total time on VMC1 005 A EC1 0.00 32.29 01 Pre-clean board (scrub and wash) 02 Dry board in oven at 85 deg. F 005 B EC1 30.00 0.48 01 Setup 02 Spread photoresist from 18000 RPM spinner 005 C EC1 30.00 2.00 01 Setup 02 Photolithography of photoresist using phototool in "real.iges" 005 D EC1 30.00 20.00 01 Setup 02 Etching of copper 005 T EC1 90.00 54.77 01 Total time on EC1 006 A MC1 30.00 4.57 01 Setup 02 Prepare board for soldering 006 B MC1 30.00 0.29 01 Setup A portion of a manufacturing process plan 4 02 Screenprint solder stop on board 006 C MC1 30 00 7 50 01 Setup

5 Modes of Planning Automated Plan Generation Mixed Initiative Planning � �

6 Example Planning Applications

Autonomous Agents for Space Exploration � Autonomous planning, scheduling, control � NASA: JPL and Ames � Remote Agent Experiment (RAX) � Deep Space 1 � Mars Exploration Rover (MER) 7

8 Not necessarily embodied! Other Autonomous Systems

Manufacturing Automation � Sheet-metal bending machines - Amada Corporation � Software to plan the sequence of bends [Gupta and Bourne, J. Manufacturing Sci. and Engr. , 1999] 9

Games E.g., Bridge Baron - Great Game Products � 1997 world champion of computer bridge [Smith, Nau, and Throop, AI Magazine , 1998] � 2004: 2nd place Us:East declarer, West dummy Opponents:defenders, South & North Finesse(P 1 ; S) Contract:East – 3NT East: ♠ KJ74 On lead:West at trick 3 West: ♠ A2 Out: ♠ QT98653 LeadLow(P 1 ; S) FinesseTwo(P 2 ; S) PlayCard(P 1 ; S, R 1 ) EasyFinesse(P 2 ; S) StandardFinesse(P 2 ; S) BustedFinesse(P 2 ; S) … … West — ♠ 2 (North — ♠ Q) (North — � 3) StandardFinesseTwo(P 2 ; S) StandardFinesseThree(P 3 ; S) FinesseFour(P 4 ; S) PlayCard(P 4 ; S, R 4 ’ ) PlayCard(P 2 ; S, R 2 ) PlayCard(P 3 ; S, R 3 ) PlayCard(P 4 ; S, R 4 ) North — ♠ 3 East — ♠ J South — ♠ 5 South — ♠ Q 10

Other Applications � Scheduling with Action Choices & Resource Requirements � Problems in supply chain management � HSTS (Hubble Space Telescope scheduler) � Workflow management � Air Traffic Control � Route aircraft between runways and terminals. Crafts must be kept safely separated. Safe distance depends on craft and mode of transport. Minimize taxi and wait time. � Character Animation � Generate step-by-step character behaviour from high- level spec � Plan-based Interfaces � E.g. NLP to database interfaces � Plan recognition 11

Other Applications (cont.) � Web Service Composition � Compose web services, and monitor their execution � Many of the web standards have a lot of connections to action representation languages � BPEL; BPEL-4WS allow workflow specifications � DAML-S allows process specifications � Business Process Composition /Workflow Management � Including Grid Services/Scientific Workflow Management � Genome Rearrangement � The relationship between different organisms can be measured by the number of “evolution events” (rearrangements) that separate their genomes � Find shortest (or most likely) sequence of rearrangements between a pair of genomes 12

Outline � Conceptual model for planning � Classes of planning problems � Classes of planners and example instances � Beyond planning � Planning research – the big picture � Some of what I hope you’ll get from the course 13

Conceptual Model 1. Environment State transition system System Σ Σ = ( S,A,E , γ ) S = {states} A = {actions} E = {exogenous events} γ = state-transition function 14

State Transition System s 1 s 0 Σ = ( S,A,E , γ ) put � S = {states} take � A = {actions} location 1 location 2 location 1 location 2 move1 move2 move1 move2 � E = {exogenous events} s 3 s 2 � State-transition function put γ : S x ( A ∪ E ) → 2 S take � S = {s 0 , …, s 5 } location 1 location 2 location 1 location 2 � A = { move1, move2, load unload put, take, load, unload } s 4 s 5 � E = {} move2 � γ : see the arrows move1 Dock Worker Robots (DWR): location 1 location 2 location 1 location 2 15

Conceptual Model 2. Controller Given observation Controller o in O , produces Observation function action a in A h : S → O s 3 location 1 location 2 16

Conceptual Model 3. Planner’s Input Planning problem Planning problem Planning problem Planner Omit unless planning is online 17

s 1 s 0 put Planning Problem take P = ( Σ , s 0 ,G ) location 1 location 2 location 1 location 2 move1 move1 move2 move2 Σ : System Description s 3 s 2 put S 0 : Initial state(s) take E.g., Initial state = s 0 location 2 location 1 location 2 location 1 load unload G: Objective s 4 s 5 Goal state, move2 Set of goal states, Set of tasks, move1 “trajectory” of states, location 1 location 2 location 1 location 2 Objective function, … E.g., Goal state = s 5 The Dock Worker Robots (DWR) domain 18

19 Instructions to the controller Planner 4. Planner’s Output Conceptual Model

s 1 s 0 Plans put take Classical plan : take location 1 location 2 location 1 location 2 a sequence of actions move1 move1 move1 move2 move2 E.g., 〈 take, move1, load, move2 〉 s 3 s 2 put Policy : partial function from S into A take E.g., location 2 location 1 location 2 location 1 load load {(s 0 , take), unload ( s 1 , move1), s 4 s 5 ( s 3 , load), move2 move2 (s 4 , move2)} move1 location 1 location 2 location 1 location 2 The Dock Worker Robots (DWR) domain 20

Outline � Conceptual model for planning � Classes of planning problems � Classes of planners and example instances � Beyond planning � Planning research – the big picture � Some of what I hope you’ll get from the course 21

Different Classes Planning Problems Varying components of the planning problem specification yields different classes of problems. E.g., dynamics: deterministic, nondeterministic, probabilistic observability: full, partial, none horizon: finite, infinite objective requirement: satisfying, optimizing … 22

Different Classes Planning Problems dynamics: deterministic , nondeterministic, probabilistic observability: full, partial, none horizon: finite , infinite objective requirement: satisfying , optimizing … � classical planning � conditional planning with full observability � conditional planning with partial observability � conformant planning � markov decision processes (MDP) � partial observable MDP (POMDP) � preference-based/over-subscription planning 23

Different Classes Planning Problems dynamics: deterministic, nondeterministic , probabilistic observability: full , partial, none horizon: finite , infinite objective requirement: satisfying , optimizing … � classical planning � conditional planning with full observability � conditional planning with partial observability � conformant planning � markov decision processes (MDP) � partial observable MDP (POMDP) � preference-based/over-subscription planning 24