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Interactive Plan Recognition
(Formerly Sequential Plan Recognition)
Reuth Mirsky, Roni Stern, Ya’akov (Kobi) Gal, Meir KalechDepartment of information systems engineering
Ben-Gurion University of the Negev
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Plan recognition“Infer the plan of theagent given observationsand a data base of recipes”Kautz (1986)
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Hypothesis representation• Plan = tree
• Hypothesis = set of plans
• Other representations (e.g. Ramírez and Geffner ‘09)3
Hypothesis representation
The output of a PR algorithm is a set of hypotheses
Mix all
Pair-wise Mix
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Number of hypotheses
51 2 3 4 5 6 7 8 9 101
10
100
1000
10000
100000
VirtualLabsTinkerPlotsSimulated
# observations
Hyp
othe
ses
Hypothesis number grows exponentially (Geib and Goldam ‘09)
The disambiguation problem
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Prior work
• Pruning • Kabanza and Filion. Controlling the hypothesis space in probabilistic plan
recognition. IJCAI. 2013. • Wiseman and Shieber. Discriminatively reranking abductive proofs for plan
recognition. Twenty-Fourth International Conference on Automated Planning and Scheduling. 2014.
• Mirsky, Gal, Shieber. CRADLE: an online plan recognition algorithm for exploratory domains. [under submission].
• Less expressive representation• Avrahami-Zilberbrand and Kaminka. Incorporating observer biases in keyhole plan
recognition (efficiently!). AAAI. Vol. 7. 2007.• Geib. Delaying Commitment in Plan Recognition Using Combinatory Categorial
Grammars. IJCAI. 2009. • Sukthankar and Sycara. Hypothesis pruning and ranking for large plan recognition
problems. AAAI. Vol. 8. 2008. 7
Contributions
• The Interactive Plan Recognition Process (IPRP)• Soundness and Completeness proof• Policies for solving IPRP• Extensive empirical evaluation
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Mirsky, Gal, Stern and Kalech. Sequential Plan Recognition. AAMAS (Extended Abstract). 2016.
Mirsky, Stern, Gal and Kalech. Sequential Plan Recognition. IJCAI. 2016.
IPRP
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Iterative process for disambiguating the hypothesis space
Coming up:•Which hypotheses to discard?•Which plan to choose?
The Refinement Criteria
Solve Problem
Create Flasks
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Solve Problem
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Pair-wise Mix
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A
B C
The Refinement Criteria
Solve Problem
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Solve Problem
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Pair-wise Mix
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A
B C
Is plan A part of the correct hypothesis?
The Refinement Criteria
Solve Problem
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Solve Problem
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Pair-wise Mix
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A
B C
Is plan A part of the correct hypothesis?
Yes, it is part of my plan
Then B and C might alsobe part of your plan
The Refinement Criteria
Solve Problem
Create Flasks
Perform Test
Write Results
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Solve Problem
Create Flasks
Perform Test
Pair-wise Mix
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Create Flasks
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A
B C
Is plan A part of the correct hypothesis?
No, it is not part of my plan
Then neither B nor C can be part of your plan
The Refinement Criteria
Solve Problem
Create Flasks
Perform Test
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Solve Problem
Create Flasks
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Pair-wise Mix
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Solve Problem
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A
B C
Is plan B part of the correct hypothesis?
The Refinement Criteria
Solve Problem
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Solve Problem
Create Flasks
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Pair-wise Mix
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A
B C
No, it is not part of my plan
Then B cannot be part of your plan, but C might
Is plan B part of the correct hypothesis?
The Refinement Criteria
Solve Problem
Create Flasks
Perform Test
Write Results
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Solve Problem
Create Flasks
Perform Test
Pair-wise Mix
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Solve Problem
Create Flasks
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A
B C
Yes, it is part of my plan
We Cannot discard hypotheses with A
Is plan B part of the correct hypothesis?
The Match Criteria
Solve Problem
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Solve Problem
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B C
A
The Match Criteria
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Solve Problem
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B C
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Is plan B part of thecorrect hypothesis?
No, Plan B is not a part of my plan
Then A and B can not be part of the
correct hypothesis
The Match Criteria
Solve Problem
Create Flasks
Create All
Perform Test
Mix All
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Solve Problem
Create Flasks
Create All
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Solve Problem
Create Flasks
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B C
AYes, Plan B is part of my plan
Then all plans can still be part of your
plan
Is plan B part of thecorrect hypothesis?
Soundness and completeness
• Completeness: IPR does not remove any hypothesis that can be refined to the correct hypothesis• Soundness: Every hypothesis IPR keeps can be
refined to the correct hypothesis
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All Hypotheses
Correct
Removed
Remaining
Which plan to query next?• Random: choose a random plan• Most Probable Hypothesis (MPH): choose a plan from the
most probable hypothesis
• Most Probable Plan (MPP): choose the most probable plan
• Entropy: choose the most informative plan
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Reduction in Hypotheses by Strategy
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Discussion
• After 1 query• 39% reduction in hypotheses (simulated domain)• 46% reduction in hypotheses (VirtualLabs domain)
• Entropy strategy outperforms all other strategies
• Find an optimal querying policy
• Apply IPRP in real world domains
• Open source code on github:
https://github.com/ReuthMirsky/IPR23
