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Adaptive Goal Recognition. Neal Lesh. Presented by Don Patterson. Goal Recognition. To infer a person’s intentions given a partial view of their actions Let A be a sequence of actions ( A*) Let G be a set of goals ( G*) A recognizer R : A* ( G* {nil}) - PowerPoint PPT Presentation
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5/10/2002
Adaptive Goal Adaptive Goal RecognitionRecognition
Neal Lesh
Presented by Don Patterson
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 25/10/2002
Goal RecognitionGoal Recognition
To infer a person’s intentions given a partial view of their actions
Let A be a sequence of actions ( A*)Let G be a set of goals ( G*)A recognizer R : A* ( G* {nil})
a program that takes an action sequence and predicts a goal or declines to make a prediction
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 35/10/2002
Adaptive Goal RecognitionAdaptive Goal Recognition
Adds modifiers to RLet T* be a set of adaptationsA recognizer RT is a recognizer that has had a subset T of T* applied to it.
To personalize we will remember a different set, T, for each person
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 45/10/2002
ExamplesExamples
A:(get pan,get egg,put butter in pan)
G*:(cook an egg, clean-up)
R(A): if (get egg) A )
then predict G = (cook an egg)else predict G = (clean-up)
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 55/10/2002
ExamplesExamples
A: (get pan, get egg, put butter in pan)
G*:(cook an egg, clean-up)
T: (put butter in pan)RT(A):
if ((get egg) A ) or ((T A) {nil})then G = (cook an egg)else G = (clean-up)
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 65/10/2002
MetricsMetrics
How are we going to compare our adapted recognizers?
Accuracy: How many times did RT(A) return the correct goal, G?Coverage: How many times did RT(A) return non-{nil}? How many times did it make a guess?Score: a function which unifies accuracy and coverage:
S(accuracy, coverage)
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 75/10/2002
Adapt the goal recognizerAdapt the goal recognizer
Let D be the training dataConsists of a set E of “episodes”An episode is a start state and a sequence of actions (S,{a1,a2,a3,…,an})
Estimate(R,D)Returns Accuracy and Coverage of R on DRunning time
Verify the true goal |E| times, once per training example.Execute the Recognizer |E| * n. Once for every action in the training example.
R({a1}
R({a1,a2}) …
R({a1,a2,a3,…, an})
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 85/10/2002
Adapt the goal recognizerAdapt the goal recognizer
Adapt(R,T,D)Greedily add adaptations to R until Estimate(RT,D) reaches a local maximum.
Running timeO(|T|2) calls to Estimate
Overall run time:O(|O(|T|T|22*[|*[|DD|*(O(Verify)+n*O(|*(O(Verify)+n*O(RRTT))])))])
O(|O(|T|T|22*|*|DD|*n*O(|*n*O(RRTT))))
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 95/10/2002
ValidationValidation
Robustness to systematic non-goal oriented actions inserted into the training data:
Example: Every time I turn on the stove I open the door to check that nothing is in it.Example: Every time I type “cd” I also type “ls” regardless of what I’m doing.
Ability of the goal recognizer to helpHow often does the computer make a correct guess?
Impact of noisy training data
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 105/10/2002
RobustnessRobustness
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 115/10/2002
RobustnessRobustness
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 125/10/2002
Ability to HelpAbility to Help
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 135/10/2002
Impact of NoiseImpact of Noise
Frequency of Abandonment
0.0 0.05 0.10 0.15 0.20
Plan length 7.2 7.5 7.5 7.9 8.6% right 82 80 80 74 63% wrong 0 0 0 0 0% skipped 18 20 20 26 37
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 145/10/2002
ObservationsObservations
Strengths:Recognizer independentProofs of trade-off between accuracy and coverageUnsupervised
590 HK Spring 2002: Adaptive Goal Recognition by Neal Lesh 155/10/2002
Challenges for Assisted Challenges for Assisted CognitionCognition
Adapt assumes discrete adaptationsProbabilities and continuous parameters are probably going to need to be handled.
Adaptations must be well chosenThis specific recognizer/adaptation pair wouldn’t appear to handle random noise wellIf actions are identified incorrectly then the system will likely fail.