Interactive Plan Recognition
Plan recognition algorithms need to maintain all candidate explanations (hypotheses) which are consistent with the observations, even though there is only a single hypothesis that is the correct one. Unfortunately, the number of possible hypotheses can be exponentially large in practice. This paper addresses the problem of how to disambiguate between many possible hypotheses that are all consistent with the actions of the observed agent. One way to reduce the number of hypotheses is to consult a domain expert or the acting agent directly about its intentions. This process can be performed sequentially, updating the set of explanations during the recognition process. The paper specifically studies how to minimize the number of queries made that are required to find the correct hypothesis. It provides a formal mapping of this problem to the probing process in model-based diagnosis and adapt a number of probing techniques for choosing which plan to query, such as maximal information gain and maximum likelihood. These approaches were evaluated on two domains from the literature using a well known plan recognition algorithm. The results showed, on all domains, that the information gain approach was able to find the correct plan using significantly fewer queries than the maximum likelihood approach as well as a baseline approach choosing random plans. Our technique can inform the design of future plan recognition systems that interleave the recognition process with intelligent interventions of their users. For the full paper see http://www.smart-society-project.eu/sequentialplanrecognition/
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Interactive Plan Recognition
- 1. 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 1
- 2. Plan recognition “Infer the plan of the agent given observations and a data base of recipes” Kautz (1986) 2
- 3. Hypothesis representation • Plan = tree • Hypothesis = set of plans • Other representations (e.g. Ramírez and Geffner ‘09) 3
- 4. Hypothesis representation The output of a PR algorithm is a set of hypotheses Mix all Pair-wise Mix 4
- 5. Number of hypotheses 51 2 3 4 5 6 7 8 9 10 1 10 100 1000 10000 100000 VirtualLabs TinkerPlots Simulated # observations Hypotheses Hypothesis number grows exponentially (Geib and Goldam ‘09)
- 7. 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
- 8. 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.
- 9. IPRP 9 Iterative process for disambiguating the hypothesis space Coming up: •Which hypotheses to discard? •Which plan to choose?
- 10. The Refinement Criteria Solve Problem Create Flasks Perform Test Write Results 10 Solve Problem Create Flasks Perform Test Pair-wise Mix Write Results Solve Problem Create Flasks Perform Test Mix All Write Results A B C
- 11. The Refinement Criteria Solve Problem Create Flasks Perform Test Write Results 11 Solve Problem Create Flasks Perform Test Pair-wise Mix Write Results Solve Problem Create Flasks Perform Test Mix All Write Results A B C Is plan A part of the correct hypothesis?
- 12. The Refinement Criteria Solve Problem Create Flasks Perform Test Write Results 12 Solve Problem Create Flasks Perform Test Pair-wise Mix Write Results Solve Problem Create Flasks Perform Test Mix All Write Results A B C Is plan A part of the correct hypothesis? Yes, it is part of my plan Then B and C might also be part of your plan
- 13. The Refinement Criteria Solve Problem Create Flasks Perform Test Write Results 13 Solve Problem Create Flasks Perform Test Pair-wise Mix Write Results Solve Problem Create Flasks Perform Test Mix All Write Results 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
- 14. The Refinement Criteria Solve Problem Create Flasks Perform Test Write Results 14 Solve Problem Create Flasks Perform Test Pair-wise Mix Write Results Solve Problem Create Flasks Perform Test Mix All Write Results A B C Is plan B part of the correct hypothesis?
- 15. The Refinement Criteria Solve Problem Create Flasks Perform Test Write Results 15 Solve Problem Create Flasks Perform Test Pair-wise Mix Write Results Solve Problem Create Flasks Perform Test Mix All Write Results 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?
- 16. The Refinement Criteria Solve Problem Create Flasks Perform Test Write Results 16 Solve Problem Create Flasks Perform Test Pair-wise Mix Write Results Solve Problem Create Flasks Perform Test Mix All Write Results A B C Yes, it is part of my plan We Cannot discard hypotheses with A Is plan B part of the correct hypothesis?
- 17. The Match Criteria Solve Problem Create Flasks Create All Perform Test Mix All Write Results 17 Solve Problem Create Flasks Create All Perform Test Write Results Solve Problem Create Flasks Perform Test Mix All Write Results B C A
- 18. The Match Criteria Solve Problem Create Flasks Create All Perform Test Mix All Write Results 18 Solve Problem Create Flasks Create All Perform Test Write Results Solve Problem Create Flasks Perform Test Mix All Write Results B C A 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
- 19. The Match Criteria Solve Problem Create Flasks Create All Perform Test Mix All Write Results 19 Solve Problem Create Flasks Create All Perform Test Write Results Solve Problem Create Flasks Perform Test Mix All Write Results 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 the correct hypothesis?
- 20. 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 20 All Hypotheses Correct Removed Remaining
- 21. 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
- 22. Reduction in Hypotheses by Strategy 22
- 23. 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 23 reuthde@gmail.com