A Smart Home Agent for Plan Recognition of Cognitively-impaired Patients

老師：張耀仁學生：徐欣佑

Introduction

Increasing problems in health field Population ageing Medical staff shortages

Smart home Cognitive assistance Plan recognition

Cognitive assistance

Cognitive deficiencies such as Alzheimer’s disease Schizophrenia

Activities of Daily Living (ADL)

Plan recognition

Identify the on-going inhabitant ADL from observed actions and events

Infer the goal pursued by the actor Predict the behaviour of the observed agent

Smart home (agent) Occupant (patient)

Plan recognition (continued)

Intended plan recognition The patient knows that he is being observed

and is adapting his behavior in order to make his intentions clear to the observer.

Keyhole plan recognition The patient does not know that he is being

observed or that he is not taking it into account

Explore solutions

Probabilistic methods Based on the Markovian model, Bayesian netw

orks and Dempster-Shafer theory The learning techniques

Build a probabilistic predictive model Logical approaches

Find series of logical deductions

The logical model of plan recognition

Lattice theory Action description logic Classified through a lattice structure Recognition space

Recognition space model

Interpret the set of the observed actions Predict the patient’s future actions (GetGun , GotoBank ) equal? (Hunt or Rob

Bank) Recognition model

Action model overview

Follows the lines of Description Logic (DL)

:next state:current stateis the precondition of expresses the effect of

Variable plan

RobBank(GotoBank & GetGun) and Hunt(GotoWood & GetGun)

GotoBank and GotoWood are incomparable A variable plan (x , GetGun) is the lower bo

und of this tow plans The substitution domain of the variable x w

ould be :

Plans composition

Stability There is no possibily of introducing other exter

nal actions Closure

Each plan must admit an upper bound and a lower bound

Plans composition (continued)

Observed action GetGun The set of possible plans is

{RobBank(GotoBank & GetGun), Hunt(GotoWood & GetGun)}

The composition of the plans {(GotoBank,GotoWood,Getgun) ,(GotoWood,

GotoBank,GetGun)}

Recognition of activities in smart home

Basic events are generated by sensors and are directly sent to agents

Low-level activity recognition (LAR) High-level recognition service (HLRS)

Achitecture of the plan recognition system

Application of activities recognition

Low-level activity recognition

High-level recognition service

High-level recognition service (continued)

Plans knowledge base WashDish(StartWasing , GotoKitchen) PrepareTea(GetWater , GotoKitchen) WatchTv(TurnOnTv , GotoLivingRoom) Drink(GetWater)

Recognition space lattice

Future experimentation

Senile Dementia of the Alzheimer’s type Kitchen Task Assessment (KTA)

Places the objects at the right place Complete description of the whole steps Follow the indications Note the patient’s errors

Conclusion

Lattice theory and action description logic Minimizing uncertainty about the prediction

of the observed behaviour Inhabitant’s specific profile Learned patient’s habits