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Plan Recognition

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Title: Plan Recognition


1
Plan Recognition
  • Henry Kautz
  • Computer Science Engineering
  • University of Washington
  • Seattle, WA

2
Food chain
  • Physical movement
  • Movement sensor fires
  • Behaviors
  • Running, grasping, lifting,
  • Plans
  • Getting a drink of water
  • Describes conventional way of achieving a goal
  • Goals
  • Quench thirst

3
Dimensions of the plan recognition problem
  • Keyhole versus interactive
  • Keyhole
  • Determine how an agents actions contribute to
    achieving possible or stipulated goals
  • Model
  • World
  • Agents beliefs
  • No model of the observer fly on the wall

4
Dimensions of the plan recognition problem
  • Keyhole versus interactive
  • Interactive
  • Agent acts in order to signal his beliefs and
    desires to other agents
  • Speech acts inform, request,
  • Discourse conventions
  • Two PIs made it to the Darpa meeting
  • Evolution of cooperation
  • Symbolic actions
  • The Statue of Liberty
  • 9/11?

5
Dimensions of the plan recognition problem
  • Ideal versus fallible agents
  • Mistaken beliefs
  • John drives to Reagan, but flight leaves Dulles.
  • Cognitive errors
  • Distracted by the radio, John drives past the
    exit.
  • Irrationality
  • John furiously blows his horn at the car in front
    of him.

6
Dimensions of the plan recognition problem
  • Reliable versus unreliable observations
  • Theres a 80 chance John drove to Dulles.
  • Open versus closed worlds
  • Fixed plan library?
  • Fixed set of goals?
  • 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

7
Dimensions of the plan recognition problem
  • Desired output
  • Set of consistent plans or goals?
  • Most likely plan or goal?
  • Most critical plan or goal?
  • Interventions observer should perform to aid or
    hinder the agent?

8
Approaches to plan recognition
  • Consistency-based
  • Hypothesize revise
  • Closed-world reasoning
  • Version spaces
  • Probabilistic
  • Stochastic grammars
  • Pending sets
  • Dynamic Bayes nets
  • Layered hidden Markov models
  • Policy recognition
  • Hierarchical hidden semi-Markov models
  • Dynamic probabilistic relational models
  • Example application Assisted Cognition

9
Hypothesize Revise
Based on psychological theories of human
narrative understanding Mention of objects
suggest hypothesis Pursue single hypothesis until
matching fails
  • The Plan Recognition Problem C. Schmidt, 1978

10
Closed-world reasoning
  • Infers the minimum set(s) of independent plans
    that entail the observations
  • Observations may be incomplete
  • Infallible agent
  • Complete plan library
  • A Formal Theory of Plan Recognition and its
    Implementation Henry Kautz, 1991

11
Version Space Algebra
  • Recognizes novel plans
  • Complete observations
  • Sensitive to noise
  • A sound and fast goal recognizer Lesh Etzioni
  • Programming by Demonstration Using Version Space
    Algebra Lau, Wolfman, Domingos, Weld.

12
Stochastic grammars
CF grammar w/ probabilistic rules Chart parsing
Viterbi Successful for highly structured tasks
(e.g. playing cards) Problems errors, context
  • Huber, Durfee, Wellman, "The Automated Mapping
    of Plans for Plan Recognition", 1994
  • Darnell Moore and Irfan Essa, "Recognizing
    Multitasked Activities from Video using
    Stochastic Context-Free Grammar", AAAI-02, 2002.

13
Pending sets
Explicitly models the agents plan agenda using
Pooles probabilistic Horn abduction
rules Handles multiple concurrent interleaved
plans negative evidence Number of different
possible pending sets can grow exponentially Conte
xt problematic? Metric time?
Pending(P,T1)? Pending(P,T),
Leaves(L), Progress(L, P, P, T1).
Happen(X,T1) ? Pending(P,T), X in P,
Pick(X,P,T1).
  • A new model of plan recognition. Goldman, Geib,
    and Miller
  • Probabilistic plan recognition for hostile
    agents. Geib, Goldman

14
Dynamic Bayes nets (I)
Models relationship between users recent actions
and goals (help needs) Probabilistic goal
persistence Programming in machine language?
  • E. Horvitz, J. Breese, D. Heckerman, D. Hovel,
    and K. Rommelse. The Lumiere Project Bayesian
    User Modeling for Inferring the Goals and Needs
    of Software Users. Proceedings of the Fourteenth
    Conference on Uncertainty in Artificial
    Intelligence, July 1998.
  • Towards a Bayesian model for keyhole plan
    recognition in large domains Albrecht, Zukermann,
    Nicholson, Bud

15
Excel help (partial)
16
Layered hidden Markov models
Cascade of HMMs, operating at different temporal
granularities Inferential output at layer K is
evidence for layer K1
  • N. Oliver, E. Horvitz, and A. Garg. Layered
    Representations for Recognizing Office Activity,
    Proceedings of the Fourth IEEE International
    Conference on Multimodal Interaction (ICMI 2002)

17
Policy recognition
Model agent using hierarchy of abstract policies
(e.g. abstract by spatial decomposition) Compute
the conditional probability of top-level policy
given observations Compiled into DBN
  • Tracking and Surveillance in Wide-Area Spatial
    Environments Using the Hidden Markov Model. Hung
    H. Bui, Svetha Venkatesh and West.
  • Bui, H. H., Venkatesh, S., and West, G. (2000) On
    the recognition of abstract Markov policies.
    Seventeenth National Conference on Artificial
    Intelligence (AAAI-2000), Austin, Texas

18
Hierarchical hidden semi-Markov models
  • Combine hierarchy (function call semantics) with
    metric time
  • Compile to DBN
  • Time nodes represent a distribution over the time
    of the next state switch
  • Linear time smoothing
  • Research issues parametric time nodes, varying
    granularity
  • Hidden semi-Markov models (segment models) Kevin
    Murphy. November 2002.
  • HSSM Theory into Practice, Deibel Kautz,
    forthcoming.

19
Dynamic probabilistic relational models
PRM - reasons about classes of objects and
relations Lattice of classes can capture plan
abstraction DPRM efficient approximate
inference by Rao-Blackwellized particle
filtering Open approximate smoothing?
  • Friedman, N., L. Getoor, D. Koller, A. Pfeffer.
    Learning Probabilistic Relational Models. 
    IJCAI-99, Stockholm, Sweden (July 1999).
  • Relational Markov Models and their Application to
    Adaptive Web Navigation, Anderson, Domingos, Weld
    2002.
  • Dynamic probabilistic relational models,
    Anderson, Domingos, Weld, forthcoming.

20
Assisted cognition
Computer systems that improve the independence
and safety of people suffering from cognitive
limitations by
  • Understanding human behavior from low-level
    sensory data
  • Using commonsense knowledge
  • Learning individual user models
  • Actively offering prompts and other forms of help
    as needed
  • Alerting human caregivers when necessary
  • http//www.cs.washingt
    on.edu/assistcog/

21
Activity Compass
  • Zero-configuration personal guidance system
  • Learns model of users travel on foot, by public
    transit, by bike, by car
  • Predicts users next destination, offers
    proactive help if lost or late
  • Integrates user data with external constraints
  • Maps, bus schedules, calendars,
  • EM approach to clustering segmenting data

The Activity Compass Don Patterson, Oren
Etzioni, and Henry Kautz (2003)
22
Activity of daily living monitor prompter
Foundations of Assisted Cognition Systems. Kautz,
Etzioni, Fox, Weld, and Shastri, 2003
23
Recognizing unexpected events using online model
selection
  • User errors, abnormal behavior
  • Select model that maximizes likelihood of data
  • Generic model
  • User-specific model
  • Corrupt (impaired) user model
  • Neurologically-plausible corruptions
  • Repetition
  • Substitution
  • Stalling

fill kettle
put kettleon stove
fill kettle
put kettleon stove
put kettlein closet
Fox, Kautz, Shastri (forthcoming)
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