Conditional Planning Section 12.4 Sec. 12.4 p.1/13 Outline Fully - PowerPoint PPT Presentation

Conditional Planning Section 12.4 Sec. 12.4 – p.1/13

Outline Fully observable environments Partially observable environments Conditional POP Sec. 12.4 – p.2/13

☞ ☛ ✞ ✝ ✂ ✞ ✁ � � � Uncertainty The agent might not know what the initial state The agent might not know the outcome of its actions The plans will have branches rather than being straight line plans conditional steps if then ✟✡✠ else ✟✡✠ ✂☎✄✆ ☛✍✌ Full observability : The agent knows what state it currently is, does not have to execute an observation action Simply get plans ready for all possible contingencies Sec. 12.4 – p.3/13

� � ✁ ✂ ✁ The vacuum world example Moving left sometimes fails Action ( Left , P RECOND : AtR , E FFECT : AtL AtR ) Only the current state can be observed include conditional effects Action ( Suck , P RECOND : ;, E FFECT : ( when AtL: CleanL ) ( when AtR: CleanR )) Actions may be both disjunctive and conditional: Moving sometimes dumps dirt on the destination square only when that square is clean Action ( Left , P RECOND : AtR ;, E FFECT : AtL ( AtL when CleanL: CleanL )) Sec. 12.4 – p.4/13

Perform and/or search Left Suck GOAL Suck LOOP Right Suck Left GOAL LOOP Sec. 12.4 – p.5/13

✁ ✁ The plan In the “double-Murphy” vacuum world, the plan is: [ Left , if AtL CleanL CleanR then [] else Suck ] Sec. 12.4 – p.6/13

� And-or Search Algorithm function A ND -O R -G RAPH -S EARCH ( problem ) returns a conditional plan , or failure O R -S EARCH ( I NITIAL -S TATE [ problem ], problem , []) function O R -S EARCH ( state, problem, path ) returns a conditional plan , or failure if G OAL -T EST [ problem ]( state ) then return the empty plan if state is on path then return failure for each action, state-set in S UCCESSORS [ problem ]( state ) do plan A ND -S EARCH ( state, problem , [ state | path ]) if plan failure then return [ action | plan ] ✁✄✂ return failure Sec. 12.4 – p.7/13

✠ ✁ ☎ ✄ ✄ ✞ ☛ ✎ ✠ ✂ ✄ ✞ ✟ ✞ ✌ � ✁ ✞ ✎ ✄ ✠ ✄ ✞ ✌ ✆ And-or Search Algorithm function A ND -S EARCH ( state, problem, path ) returns a conditional plan , or failure in state-set do for each �✂✁ O R -S EARCH ( , problem, path ) ☎✝✆ if plan failure then return failure return [ if �✡✠ then else if then ☎✝✆ �☞☛ else ifelse . ..if �✍✌ then ☎✝✆ ] else ☎✝✆ Sec. 12.4 – p.8/13

Triple Murhpy vacuum world The vacuum cleaner sometimes deposits dirt when it moves to a clean destination square It sometimes deposits dirt if suck is applied to a clean square + move sometimes fails Sec. 12.4 – p.9/13

First level of the search Left Suck GOAL Sec. 12.4 – p.10/13

✁ � Triple Murphy vacuum world No acyclic solutions A cyclic solution is to try going left until it works. Use a label . [ : Left , if atR then else if CleanL then [] else Suck ] �✂✁ Sec. 12.4 – p.11/13

Partially observable environments The agent knows only a certain amount of the actual state (e.g., local sensing only, does not know about the other squares) Automatic sensing : at every time step the agent gets all the available percepts Active sensing : percepts are obtained only by executing specific sensory actions “Alternate double Murphy world”: dirt can sometimes be left behind when the agent leaves a clean square Belief state : The set of possible states that the agent can be in Sec. 12.4 – p.12/13

Part of the search Left CleanL ~CleanL Suck Right CleanR ~CleanR Suck Sec. 12.4 – p.13/13