Computer Science CPSC 322 Lectur ture e 12 Planni anning: ng: Intro and For Forward Planning, Slide 1
Announ nouncem emen ents • Material for midterm available in Connect 1. List of Learning Goals 2. Short questions on material (no solutions) 3. Sample problem-solving questions (with solutions) Material covered • • Until Forward Planning included (covered today) • See corresponding learning goals and short questions on Connect Midterm will be close textbook, no calculator or other devices • - Part short questions similar or even verbatim from the list posted in connect - Part more problem-solving style questions There will be an individual exam followed by a group exam on the same • test • Groups will be formed on the spot, not predefined
Exam Format Form Group Exam Collect Indiv. Exam Groups (same or subset of Indiv. Exam)
Lect cture re O Overvi rview • Planning: Intro • STRIPS representation • Forward Planning • Heuristics for Forward Planning
Course Overview Representation Environment Reasoning Stochastic Deterministic Technique Problem Type Arc Consistency Constraint Vars + Search Satisfaction Constraints Static Belief Nets Logics Query Variable Search Elimination Decision Nets Sequential STRIPS Variable Planning Elimination Search Markov Processes First Part of Value the Course Iteration 5
Course Overview Representation Environment Reasoning Stochastic Deterministic Technique Problem Type Arc Consistency Constraint Vars + Search Satisfaction Constraints Static Belief Nets Logics Query Variable Search Elimination Decision Nets Sequential STRIPS Variable Planning Elimination Search Markov Processes We’ll focus Value on Planning Iteration 6
Planni anning P ng Probl oblem em • Goal • Description of states of the world • Description of available actions => when each action can be applied and what its effects are • Planni anning ng: build a sequence of actions that, if executed, takes the agent from the current state to a state that achieves the goal But ut, hav haven’ en’t w we e seen t een thi his bef before? Yes es, i in n sear earch, but but w we’ e’ll look ook at at a a new new R&R sui uitab able e for or pl planni anning ng Slide 7
Standard Search vs. Specific R&R systems • Constraint Satisfaction (Problems): • State: assignments of values to a subset of the variables • Successor function: assign values to a “free” variable • Goal test: all variables assigned a value and all constraints satisfied? • Solution: possible world that satisfies the constraints • Heuristic function: none (all solutions at the same distance from start) • Planning : • State • Successor function • Goal test • Solution • Heuristic function • Inference • State • Successor function • Goal test • Solution • Heuristic function 8
Standar andard S d Sear earch v ch vs. Spec pecific R c R&R s syst ystem ems CSP problems had some specific properties • States are represented in terms of features (variables with a possible range of values) • Goal: no longer a black box => expressed in terms of constraints (satisfaction of) • But actions are limited to assignments of values to variables • No notion of path to a solution: only final assignment matters Slide 9
Key I Idea ea of Planni nning ng • “ Open-up ” the representation of states, goals and actions – Both states and goals as set of features – Actions as preconditions and effects defined on state features • agent can reason more deliberately about which actions to consider to achieve its goals. Slide 10
Key I Idea ea of Planni nning ng • This representation lends itself to solve planning problems either • As pure search problems • As CSP problems • We will look at one technique for each approach • t his will only scratch the surface of planning techniques • but will give you an idea of the general approaches in this important area of AI Slide 11
Planning Techniques and Application from: • Ghallab, Nau, and Traverso Automated Planning: Theory and Practice Morgan Kaufmann, May 2004 ISBN 1-55860-856-7 • Web site: http://www.laas.fr/planning applications 12
Let’s start by introducing a very simple planning problem, as our running example Slide 13
Runni unning ng Exam ampl ple: D Del eliver very R Robot obot ( (text extboo book) k) • Consider a del delivery robo robot nam named ed Rob ob, who must navigate the following environment, and can deliver coffee and mail to Sam, in his office Slide 14
Deliv livery R Robot E t Example le: fe features • RLoc - Rob's location • Domain: {coffee shop, Sam's office, mail room, lab} short {cs, off, mr, lab} • RHC – Rob has coffee • Domain: {true, false}. Alternatively notation for RHC = T/F: rhc indicates that Rob has coffee, and that Rob doesn't’have coffee rhc • SWC – Sam wants coffee {true, false} • MW – Mail is waiting {true, false} • RHM – Rob has mail {true, false} • An example state is 15
Deliv livery R Robot E t Example le: fe features • RLoc - Rob's location • Domain: {coffee shop, Sam's office, mail room, lab} short {cs, off, mr, lab} • RHC – Rob has coffee • Domain: {true, false}. Alternatively notation for RHC = T/F: rhc indicates that Rob has coffee, and that Rob doesn't’have coffee rhc • SWC – Sam wants coffee {true, false} • MW – Mail is waiting {true, false} • RHM – Rob has mail {true, false} • An example state is Rob is in the lab, it does not have coffee, Sam wants coffee, there is no mail waiting and Rob has mail 16
Deliv livery R Robot E t Example le: Actio tions The robot’s actions are: puc - Rob picks up coffee • must be at the coffee shop and Preconditions for not have coffee action application delC - Rob delivers coffee • must be at the office, and must have coffee pum - Rob picks up mail • must be in the mail room, and mail must be waiting delM - Rob delivers mail • must be at the office and have mail move - Rob's move actions – there are 8 of them move clockwise ( mc-x ), move anti-clockwise ( mcc-x ) • from location x (where x can be any of the 4 rooms) must be in location x • 17
Model deling a ng actions ons f for planni anning ng • The key to sophisticated planning is modeling actions • Leverage a feature-based representation: • Model when actions are possible, in terms of the values of the features in the current state • Model state transitions caused by actions in terms of changes in specific features 18
Lect cture re O Overvi rview • Planning: Intro • STRIPS representation • Forward Planning • Heuristics for Forward Planning
STRIPS r repr pres esent ntat ation on (ST STanf anfor ord Res esear earch h Insti titu tute te Probl oblem em Solver er ) STRIPS - the planner in Shakey, first AI robot http://en.wikipedia.org/wiki/Shakey_the_robot In STRIPS, an action has two parts: 1. Preconditions: a set of assignments to features that must be satisfied in order for the action to be legal/valid/applicable 2. Effects: a set of assignments to features that are caused by the action 20
STRIPS act ST ctions: Exa Example STRIPS representation of the action pick up coffee, puc : pr prec econdi ditions ons Loc = and RHC = • effe fects ts RHC = • cs = coffee shop off = Sam’s office mr = mail rom 21
STRIPS act ST ctions: Exa Example STRIPS representation of the action pick up coffee, puc : prec pr econdi ditions ons Loc = cs and RHC = F • effe fects ts RHC = T • cs = coffee shop off = Sam’s office mr = mail rom STRIPS representation of the action deliver coffee, Del : pr prec econdi ditions ons Loc = and RHC = • effe fects ts RHC = and SWC = • 22
ST STRIPS act ctions: Exa Example STRIPS representation of the action pick up coffee, puc : pr prec econdi ditions ons Loc = cs and RHC = F • effe fects ts RHC = T • cs = coffee shop off = Sam’s office mr = mail rom STRIPS representation of the action deliver coffee, Del : pr prec econdi ditions ons Loc = off and RHC = T • effe fects ts RHC = F and SWC = F • Not ote e in this domain Sam doesn't have to want coffee for Rob to deliver it; one way or another, Sam doesn't want coffee after delivery. 23
STRIPS ac actions: MC MC and and MA MAC STRIPS representation of the actions related to moving clockwise • mc-cs prec econd onditions ns Loc = cs effects Loc = off • mc-off prec econd onditions ns Loc = off cs = coffee shop effects Loc = labf off = Sam’s office • mc-lab …. mr = mail rom • mc-mc … There are 4 more actions for Move Counterclockwise (mcc-cs, mcc-off, etc.) 24
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