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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.

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Presentation on theme: "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."— Presentation transcript:

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… 1

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 2

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 3

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

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

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


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

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. 9

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 others inferences 10

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 doctors appointment during her office hours. Irrationality John furiously blows his horn at the car in front of him. 11

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? 12

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? 13

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? 14

15 Other dimensions Reliable versus unreliable observations Theres 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. 15

16 Dimensions Relation to agent Model of agent Goals and plans Observ- ations InferModel (library) Produce Intended(possibly) Irrational StaticNoisyActivityIncompleteThe Answer KeyholePartial knowledge PartialGoalBest answer Adversar- ial Homo Econom- icus DynamicCompletePlanCompleteDistribu- tion Next action 16


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 18

19 Hypothesize & Revise The Plan Recognition Problem C. Schmidt, 1978 The Plan Recognition Problem Related work from Yale AI Lab: Cullingfords Script Applier Mechanism, Wilenskys 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 19

20 Closed-world reasoning A Formal Theory of Plan Recognition and its Implementation Henry Kautz, 1991 A Formal Theory of Plan Recognition and its Implementation 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 20

21 Parsing Vilain 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) 21

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

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 SprinklerRaining Grass wet 23

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 A Bayesian Theory of Plan Recognition, Charniak and Goldman, AIJ, Interpretation as Abduction, Hobbs, Stickel, Martin & Edwards, Proc. ACL, Jack went to the liquor store. Was he shopping? 24

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 (dAmbrosio, Provan, Charniak & Goldman) Horn logic (Poole) 25

26 Pending sets A new model of plan recognition. Goldman, Geib, and Miller,1999 A new model of plan recognition. A probabilistic plan recognition algorithm based on plan tree grammars, Geib and Goldman, AIJ, Explicitly models the agents plan agenda using Pooles 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 Happen(X,T+1) Pending(P,T), X in P, Pick(X,P,T+1). Pending(P,T+1) Pending(P,T), Leaves(L), Progress(L, P, P, T+1). 26

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


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? 29

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 30

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 31

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

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 33

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

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. 35

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 36

37 Coreference and quantification In some domains we dont 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 37

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, its hard to get something for nothing. 38

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. 39

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

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

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

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


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