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Welcome

Welcome. Welcome to the Dagstuhl seminar on Plan Recognition Please upload titles for the talks you want to give We would like everyone to have an opportunity to give a short talk We have some panel ideas, but these are open to reconsideration – contact me

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Welcome

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  1. Welcome • Welcome to the Dagstuhl seminar on Plan Recognition • Please upload titles for the talks you want to give • We would like everyone to have an opportunity to give a short talk • We have some panel ideas, but these are open to reconsideration – contact me • We will be scheduling incrementally • Scheduled through tomorrow… • Schedules will be re-posted as updated…

  2. Panel ideas • Should there be a plan recognition competition? • Rational versus fallible agents • Activity recognition, behavior recognition, plan recognition, goal recognition • Oh, my! • Full and partial observability • Generative versus plan library approaches

  3. Schedule: Monday • AM: Welcome and survey • PM: • Jerry Hobbs: discourse and plan recognition • Short talks • George Ferguson • Matthew Stone • Chris Baker: plan recognition and psychology • Panel: a plan recognition competition? • Evening: get acquainted event

  4. Schedule: Tuesday • AM: • Kathy Laskey: probabilistic methods for PR • Short talks • FrodualdKabanza • Francis Bisson • GitaSukthankar • PM: • Tom Dietterich: learning and plan recognition • Short talks • David Pattison • Nate Blaylock • Panel: Rational versus fallible agents?

  5. Plan RecognitionHistorical Survey Henry Kautz University of Rochester Robert P. Goldman SIFT, LLC Old school plan recognition… Dagstuhl, April 2011

  6. Outline • Dimensions of the plan recognition problem • Historical survey of methods • Challenges

  7. Dimensions of Plan recognition

  8. Keyhole, intended and adversarial plan recognition • Keyhole • Observer non-intrusively watches the agent • Determine how an agent’s actions contribute to achieving possible or stipulated goals • Model • World • Agent’s beliefs

  9. Keyhole, intended and adversarial plan recognition • Intended recognition • Agent acts in order to signal his beliefs and desires to other agents • Speech acts – inform, request, … • Discourse conventions • “The 3:15 train to Windsor?” • “Gate 10” [Allen & Perrault] • Symbolic actions • The Statue of Liberty • 9/11? • The agent may require a model of the observer.

  10. Keyhole, intended and adversarial plan recognition • Adversarial • Agent acts in order manipulate the observer • Deception, bluffing, misdirection, etc. … • Agent and observer will need sophisticated models of each other’s inferences

  11. Ideal versus fallible agents • Mistaken beliefs • John drives to Reagan, but flight leaves from Dulles. • The doctor bleeds the patient to cure disease. • Cognitive errors • Distracted by the radio, John drives past the exit. • Jill schedules a doctor’s appointment during her office hours. • Irrationality • John furiously blows his horn at the car in front of him.

  12. Output of plan recognition • Activity recognition • Simply identify a known behavior pattern • Goals • Recognize the objective, but not the specific recipes used • Plans • Next action the agent will take? • Best action to aid or counter the agent?

  13. Output of plan recognition: likelihood • Likelihood… • Most likely interpretation? • Distribution over plans and goals? • The above have subtly different strengths and weaknesses… • Most critical plan or goal?

  14. Richness of plans • Are actions atomic? • Or do they have parameters? • Structure (e.g., cases)? • Do plans have structure and parameters? • Coreference? • The patient of the plan will be the destination of step one and the patient of step two… • Are there plan libraries at all?

  15. Other dimensions • Reliable versus unreliable observations • “There’s a 80% chance John drove to Dulles.” • Open versus closed worlds • Fixed plan library? • Fixed set of goals? • Fixed set of entities? • Metric versus non-metric time • John enters a restaurant and leaves 1 hour later. • John enters a restaurant and leaves 5 minutes later. • Single versus multiple ongoing plans • “White knights” • Static versus evolving set of intentions • Abandoning goals: I was going to drive to the store, but the weather was too bad. • Reacting to opportunities: I was going by the playroom on the way from the laundry, so I picked up the toys.

  16. Dimensions

  17. METHODS

  18. Earliest work • Generally in service of language understanding • Often narrative understanding • Understanding indirect speech acts • Allen & Perrault, “Analyzing Intention in Utterances,” AI, 1980 • Rich vein of work using plan recognition in dialog understanding and IUI • Will be hearing more from George Ferguson later today! • Methodologically: Mostly shared early enthusiasm for rule-based systems

  19. Hypothesize & Revise • The Plan Recognition Problem C. Schmidt, 1978 • Related work from Yale AI Lab: Cullingford’s Script Applier Mechanism, Wilensky’s PAM, etc., 1978 • Charniak, Ms. Malaprop, 1978 – Frame-based and used TMS Based on psychological theories of human narrative understanding Mention of objects suggest hypothesis Pursue single hypothesis until matching fails

  20. Closed-world reasoning • A Formal Theory of Plan Recognition and its Implementation Henry Kautz, 1991 • Infers the minimum set(s) of independent plans that entail the observations • Observations may be incomplete • Infallible agent • Complete plan library • Limited to pasta preparation

  21. Parsing • Vilain 1990 --- use parsing results to characterize computational complexity of plan recognition • There were earlier attempts to parse plans • Parsing techniques closely related to Closed-world reasoning (Built on Kautz and Allen) • Find an explanation that covers all of the observations • Parsing techniques deal poorly with partial ordering, worse with interleaving • Leads to: • Later work on stochastic parsing (Pynadath and Wellman) • Attempts to exploit exotic parsing techniques (Geib)

  22. Abduction • Reason from effect to cause (C.S. Peirce) • Explanation • Diagnosis • People: • Charniak • Hobbs et al., TACITUS • Leads to interest in Bayes nets

  23. Bayes Nets • DAG-structured models of probability distributions • Came into the fore for diagnostic applications • Challenge: Static Bayes nets for complex domains can be extremely large Raining Sprinkler Grass wet

  24. Bayes Nets • Knowledge Based Model Construction: Dynamically build Bayes nets showing how plans explain actions • Multiple goals • Abstraction hierarchies • Equality reasoning for coreference • Poor treatment of time “Jack went to the liquor store.” Was he shopping? • “A Bayesian Theory of Plan Recognition,”Charniak and Goldman, AIJ, 1993. • “Interpretation as Abduction,” Hobbs, Stickel, Martin & Edwards, Proc. ACL, 1988.

  25. More on Bayes net methods • Laskey and her colleagues have worked on military domains • Further developed KBMC techniques (e.g. query completeness); coreference, identity uncertainty • Many related techniques • E.g., Hobbs et al. cost-based abduction • ATMSes (d’Ambrosio, Provan, Charniak & Goldman) • Horn logic (Poole)

  26. Pending sets Explicitly models the agent’s “plan agenda” using Poole’s “probabilistic Horn abduction” rules Bridge between Bayes net and HMM frameworks Handles multiple concurrent interleaved plans & negative evidence Number of different possible pending sets can grow exponentially • A new model of plan recognition. Goldman, Geib, and Miller,1999 • “A probabilistic plan recognition algorithm based on plan tree grammars,”Geib and Goldman, AIJ, 2009. Pending(P’,T+1) Pending(P,T), Leaves(L), Progress(L, P, P’, T+1). Happen(X,T+1)  Pending(P,T), X in P, Pick(X,P,T+1).

  27. Version Space Algebra • A sound and fast goal recognizer Lesh & Etzioni, IJCAI 1995 • Programming by Demonstration Using Version Space Algebra Lau, Wolfman, Domingos, Weld. • Related to later work on plan-recognition through planning • Recognizes novel plans • Complete observations

  28. CHALLENGES

  29. Evaluation • Ground truth • Difficult to get labeled data • Epistemic question --- do our proposed labelings correspond to any real ground truth? • Prediction tasks • Next action? • Future action? • Good choice of assistive action? Countermeasure? • Can prediction act as proxy for ground truth?

  30. Epistemic question • What is the status of the recipes that we postulate as explanations for actions? • Are they taken as being real in some sense? • Corresponding to mental contents? • Identified regularities that really exist in the world? • Data structures that just exist for our convenience

  31. Computational difficulties • Computational complexity • Theoretical results • Practical results • Challenges from domains • Some domains inherently ambiguous • Adversarial reasoning • Do we need game-theoretic reasoning • Cooperative as well as adversarial

  32. Plan libraries • Engineered? • Learned? • Something in between? • Learned ones often seem impoverished • Engineering seems impossible!

  33. Learning • Structural learning • Learn the contents of plan libraries (in one form or another) • Parameter learning • Adjust parameters of known libraries • Both offer challenges related to those of evaluation • Plan recognition may be done in service of learning, as well as the other way around. • Infer goals to learn novel recipes

  34. Imperfections • Imperfect agents • Imperfect information • Imperfect reasoning • Imperfect task performance • Challenging for non-empirical algorithms • Imperfect observations • Imperfect models • Including seemingly-irrelevant actions

  35. User models • In many domains, the behaviors exhibited are not just a function of the actions, goals and plans, but agent characteristics, as well. • Developing clean ways to combine agent-dependent and – independent information is a challenge going forward. • Often per-agent training is unacceptable.

  36. Sensing • In many cases it is difficult to sense the agents’ actions: • Labeling actions in primitive sensor data • Vision • Network packets • Linguistic utterances • Hardware/software hybrid systems • E.g., oil refinery --- user can go out and use a wrench un-observed • Conventional software • Even Horvitz et al. report difficulties “seeing” actions of Microsoft Office users • Mixed streams • Individual actions in network packet streams

  37. Coreference and quantification • In some domains we don’t have object identity and permanence and the number of agents simply handed to us. • Story understanding • Military situation interpretation • Identity hypotheses enter into plan recognition

  38. Anomaly detection • Often appealed to as a solution for detecting some phenomenon that is difficult to model: • Intrusion behavior in computer security • Terrorist behavior in tracking and camera data • Dementia-induced behavior in tracking elderly subjects • Accuracy requires deep understanding of the models’ properties • Stationarity (often violated in computer security) • “Size” and “shape” of normal behaviors • As always, it’s hard to get something for nothing.

  39. The Role of State • Many (but not all) plan recognition systems represent only the state of the planning agent. • The state of the environment is modeled implicitly, if at all.

  40. Groups • Teamwork • Friendly: recognize teammates’ intentions to coordinate and aid • Hostile: recognize opponents’ intentions to hinder and obstruct • Role recognition

  41. Hypothesis retrieval • Some early work assumed that there were enough candidate hypotheses that retrieval could be an issue

  42. Predictive and explanatory inference • A lot of concern in early work about combining top-down and bottom-up inference

  43. Actions with weak diagnostic power • E.g., computer security • We would desperately like to know the attacker’s motivations • But what do we do with • Get access to the target • Gain administrator privileges on the target…

  44. Coffee and then henry’s turn…

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