Hypothesis number grows exponentially. (Geib and Goldam '09) ... B. C. Is plan A part of the correct hypothesis? No, it
Interactive Plan Recognition (Formerly Sequential Plan Recognition) Reuth Mirsky, Roni Stern, Ya’akov (Kobi) Gal, Meir Kalech Department of information systems engineering Ben-Gurion University of the Negev
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Plan recognition “Infer the plan of the agent given observations and 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)
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Hypothesis representation The output of a PR algorithm is a set of hypotheses
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Number of hypotheses Hypothesis number grows exponentially (Geib and Goldam ‘09) 100000 Hypotheses
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VirtualLabs TinkerPlots Simulated
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# observations
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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.
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Contributions • The Interactive Plan Recognition Process (IPRP) • Soundness and Completeness proof • Policies for solving IPRP • Extensive empirical evaluation
8 Mirsky, Gal, Stern and Kalech. Sequential Plan Recognition. AAMAS (Extended Abstract). 2016. Mirsky, Stern, Gal and Kalech. Sequential Plan Recognition. IJCAI. 2016.
IPRP Iterative process for disambiguating the hypothesis space Coming up: •Which hypotheses to discard? •Which plan to choose?
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The Refinement Criteria A Create Flasks
B
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The Refinement Criteria Is plan A part of the correct hypothesis?
A Create Flasks
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The Refinement Criteria Is plan A part of the correct hypothesis? A Yes, it is part of my plan
Then B and C might also be part of your plan B
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The Refinement Criteria Is plan A part of the correct hypothesis? A No, it is not part of my plan Then neither B nor C can be part of your plan B
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The Refinement Criteria Is plan B part of the correct hypothesis? A Create Flasks
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The Refinement Criteria Is plan B part of the correct hypothesis? A No, it is not part of my plan Then B cannot be part of your plan, but C might
B
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The Refinement Criteria Is plan B part of the correct hypothesis? A Yes, it is part of my plan We Cannot discard hypotheses with A
B
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The Match Criteria B
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The Match Criteria B
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Is plan B part of the correct hypothesis? No, Plan B is not a part of my plan Then A and B can not be part of the correct hypothesis
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The Match Criteria B
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Is plan B part of the correct hypothesis? Yes, Plan B is part of my plan Then all plans can still be part of your plan
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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 All Hypotheses
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Correct
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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 21
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/IPR
[email protected]
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