Groupe de Recherche en Informatique Université du Québec à Chicoutimi A Logical Approach to ADL...

Groupe de Recherche en InformatiqueGroupe de Recherche en InformatiqueUniversité du Québec à ChicoutimiUniversité du Québec à Chicoutimi

A Logical Approach to ADL A Logical Approach to ADL Recognition for Alzheimer’s patientsRecognition for Alzheimer’s patients

ICOST 2006ICOST 2006

Bruno Bouchard, Abdenour Bouzouane, Sylvain GirouxBruno Bouchard, Abdenour Bouzouane, Sylvain Giroux

Laboratoire DOMUSLaboratoire DOMUSUniversité de SherbrookeUniversité de Sherbrooke

http://www.usherbrooke.ca/

PlanPlan

2. Related Works

3. Lattice Recognition Model

4. Smart Home Validation

1. Cognitive Assistance Context

5. Conclusion and Future Work

CAN-AI’06, Québec, Canada, 2006



Cognitive assistanceCognitive assistance

• Predict the patient’s behaviour

• Identify opportunities for assistance

Activities of Daily Living RecognitionActivities of Daily Living Recognition



Difficulties in recognitionDifficulties in recognition

Patient’s symptoms may lead to Patient’s symptoms may lead to incoherent behaviourincoherent behaviour

Observations are not necessarily relatedObservations are not necessarily related

(Kautz, 1991)(Kautz, 1991)


Logical approaches to plan recognitionLogical approaches to plan recognition

(Kautz, 1991)(Kautz, 1991)• First-order logic & Circumscription theory

• Situations theory

DisadvantagesDisadvantages

• Assume the rationality of the observed agent

• All possible plans are considered equiprobable

Related WorksRelated Works

(Wobke, 2002)(Wobke, 2002)


• Markovian model• Bayesian networks

(Boger et al., 2002)(Boger et al., 2002)

(Albrecht et al., 1998)(Albrecht et al., 1998)

Related WorksRelated Works

(Patterson et al., 2003)(Patterson et al., 2003)

(Bauchet et al., 2005)(Bauchet et al., 2005)

Probabilistic and Learning methodsProbabilistic and Learning methods

DisadvantagesDisadvantages

• Highly dependent on the application context

• Might lead to learn inconsistent patterns

• Need a large amount of training data.


, , , ,p DisU, , M A

A

p

DisU

= Set of basic actions

= Sequence operator

= Subsumption relation of plans

= Composition operation

= Disunification operation

= Lattice upper bound operation

= Lattice lower bound operation

Lattice recognition model based Lattice recognition model based on Action Description Logicon Action Description Logic

Algebraic tools structuring the recognition process


Possible plansPossible plans = { , , ... } P

= { | ( ) }O o a a o A

= Plan library

= Set of observed actions

• A plan is a possible plan for an observed action a(o) if a and thus: ( )o

| s a, o O a o P P

The patient’s intention can go beyond The patient’s intention can go beyond the possible plans setthe possible plans set


Disunification operationDisunification operation

( )( , )

{ , }

A a ac, iff c c a and c b

DisU a b = x, otherwise Sub x = a b, a b, b a

DisU is an injective application X x X A A A

• is a intention schema if there exist a substitution such that is a plausible plan.

1( ... ... )

na x a

A 2 1

( ... ( ) ... )n

a x a

Intention schemaIntention schema

x characterizes the uncertainty of the prediction


Composition operationComposition operationA composition is a set of new plans that are not pre-established satisfying the consistency properties

Substitution domainSubstitution domain

The substitution domain Sub(x) of a variable x X is a subset Sub(x)

A substitution is an application : X 2A, represented by a set of variable-actions pairs: {x a1, y a2oa3,…},

where a1 Sub(x) and a2 o a3 Sub(y).

A

Substitution processSubstitution process


Recognition spaceRecognition space, ,O

p= , LR P

• The set of plausible plans ordered by the subsumption relation forms a lattice structure

OLP

p

p

We have if there is a substitution {x ai, y bj, …} such that i

[ 1, || ], (aibi) X aibiwhere|| is the cardinality of

pp

=

=

The upper bound is the most specific common partial plan subsumer cr … c1, such that i 1, rwith k r min(n, m)oj [1, k], ai,

bi X then cj(oj ), oj O, ai ci and bi ci. p p

=

1 1 1

1 1 1

( ) ( ) ...

( ) ( ) ...

m n+1 n n k k k

n m+1 m m k k k

b ... b DisU a , b ... DisU a , b , if n m

b ... b DisU a , b ... DisU a , b , if m n

o o

o o


Smart Home ValidationSmart Home Validation



First resultsFirst results

• Simulation of real case scenarios

• Behavioural recognition accuracy of 47%

(Baum et al., 1993)(Baum et al., 1993)


Conclusion and future workConclusion and future work

Our formal frameworkOur formal framework

• Structure the recognition process• Minimize the uncertainty in the predictions

Future workFuture work

• Use contextual information and the patient's profile to attribute a probability to plans



Thank you for your attention!Thank you for your attention!

Questions?Questions?

You want to know more…You want to know more…

http://http://domus.usherbrooke.cadomus.usherbrooke.ca

(Albrecht et al., 1998) Albrecht D.W., Zukerman I., Nicholson A.: Bayesian Models for Keyhole Plan Recognition in an Adventure Game, User Modelling and User-Adapted Interaction, Vol. 8., (1998), 5-47.

(Bauchet et al., 2005) Bauchet J., Mayers A.: Modelisation of ADL in its Environment for Cognitive Assistance, In: Proc. of the 3rd International Conference on Smart homes and health Telematics, ICOST'05, Sherbrooke, Canada, (2005), 3-10.

(Baader et al., 2003) Baader F., Calvanese D., McGuiness D., Nardi D. et Patel-Schneider P.: The Description Logic Handbook: Theory, Implementation, and applications. Cambridge University Press, United Kingdom, (2003).

(Boger et al., 2005) Boger J., Poupart P., Hoey J., Boutilier C., Fernie G. and Mihailidis A.: A Decision-Theoretic Approach to Task Assistance for Persons with Dementia. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI'05), pages 1293-1299, Edinburgh, Scotland, (2005).

(Kautz, 1991) Kautz H.: A Formal Theory of Plan Recognition and its Implementation, Reasoning About Plans, Allen J., Pelavin R. and Tenenberg J. eds., Morgan Kaufmann, San Mateo, C.A., (1991), 69-125.

(Patterson et al., 2003) Patterson D., Liao L., Fox D., Kautz H: Inferring High-Level Behavior from Low-Level Sensors. In Dey, A. Schmidt A. and McCarthy J. F., eds., Proc. of UBICOMP'03: The 5th Int. Conf. On Ubiquitous Computing, volume LNCS 2864, (2003) 73–89. Springer-Verlag.

(Wobke, 2002) Wobke W.: Two Logical Theories of Plan Recognition, Journal of Logic Computation, Vol. 12 (3), (2002), 371-412.

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Groupe de Recherche en Informatique Université du Québec à Chicoutimi A Logical Approach to ADL...

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