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

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

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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 event3

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

Plan RecognitionHistorical Survey

Henry KautzUniversity of Rochester

Robert P. GoldmanSIFT, LLC

Dagstuhl, April 20115

Old school plan recognition…

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

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DIMENSIONS OF PLAN RECOGNITION

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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

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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

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

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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.

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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?

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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?

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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?

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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.15

Dimensions

Relation to agent

Model of agent

Goals and plans

Observ-ations

Infer Model (library)

Produce

Intended (possibly) Irrational

Static Noisy Activity Incomplete “The Answer”

Keyhole Partial knowledge

Partial Goal Best answer

Adversar-ial

Homo Econom-icus

Dynamic Complete Plan Complete Distribu-tion

Next action

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METHODS

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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

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 understandingMention of objects suggest hypothesisPursue single hypothesis until matching fails

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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

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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)

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Abduction Reason from effect to cause (C.S. Peirce)

Explanation Diagnosis

People: Charniak Hobbs et al., TACITUS

Leads to interest in Bayes nets

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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

Grasswet

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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, 1993.• “Interpretation as Abduction,” Hobbs, Stickel, Martin & Edwards, Proc. ACL,

1988.

“Jack went to the liquor store.”Was he shopping?

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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)

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Pending sets

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.

Explicitly models the agent’s “plan agenda” using Poole’s “probabilistic Horn abduction” rulesBridge between Bayes net and HMM frameworksHandles multiple concurrent interleaved plans & negative evidenceNumber 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).

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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

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CHALLENGES

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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?

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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

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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

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Plan libraries Engineered? Learned? Something in between?

Learned ones often seem impoverished Engineering seems impossible!

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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

Imperfections Imperfect agents

Imperfect information Imperfect reasoning Imperfect task performance Challenging for non-empirical algorithms

Imperfect observations Imperfect models

Including seemingly-irrelevant actions

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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.

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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

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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

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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.38

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.

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Groups Teamwork

Friendly: recognize teammates’ intentions to coordinate and aid

Hostile: recognize opponents’ intentions to hinder and obstruct

Role recognition

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Hypothesis retrieval Some early work assumed that there were

enough candidate hypotheses that retrieval could be an issue

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Predictive and explanatory inference A lot of concern in early work about

combining top-down and bottom-up inference

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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…

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COFFEE AND THEN HENRY’S TURN…

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