adaptive goal recognition

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


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


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)


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)


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)


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})


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))))


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


RobustnessRobustness


Ability to HelpAbility to Help


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


ObservationsObservations

Strengths:Recognizer independentProofs of trade-off between accuracy and coverageUnsupervised


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.

adaptive goal recognition

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