Groupe de Recherche en Informatique Université du Québec à Chicoutimi A Logical Approach to ADL Recognition for Alzheimer’s patients ICOST 2006 Bruno Bouchard, Abdenour Bouzouane, Sylvain Giroux Laboratoire DOMUS Université de Sherbrooke

Plan 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

CAN-AI’06, Québec, Canada, 2006 Cognitive assistance Predict the patient’s behaviour Identify opportunities for assistance Activities of Daily Living Recognition Activities of Daily Living Recognition

CAN-AI’06, Québec, Canada, 2006 Difficulties in recognition Patient’s symptoms may lead to incoherent behaviour Patient’s symptoms may lead to incoherent behaviour Observations are not necessarily related Observations are not necessarily related (Kautz, 1991)

CAN-AI’06, Québec, Canada, 2006 Logical approaches to plan recognition Logical approaches to plan recognition (Kautz, 1991) First-order logic & Circumscription theory Situations theory Disadvantages Disadvantages Assume the rationality of the observed agent All possible plans are considered equiprobable Related Works (Wobke, 2002)

CAN-AI’06, Québec, Canada, 2006 Markovian model Bayesian networks (Boger et al., 2002) (Albrecht et al., 1998) Related Works (Patterson et al., 2003) (Bauchet et al., 2005) Probabilistic and Learning methods Probabilistic and Learning methods Disadvantages Disadvantages Highly dependent on the application context Might lead to learn inconsistent patterns Need a large amount of training data.

CAN-AI’06, Québec, Canada, 2006 = 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 on Action Description Logic Algebraic tools structuring the recognition process

CAN-AI’06, Québec, Canada, 2006 Possible plans = Plan library = Set of observed actions A plan  is a possible plan for an observed action a(o) if a  and thus: The patient’s intention can go beyond the possible plans set The patient’s intention can go beyond the possible plans set

CAN-AI’06, Québec, Canada, 2006 Disunification operation DisU is an injective application  X x   X i s a intention schema if there exist a substitution such that is a plausible plan. Intention schema Intention schema x characterizes the uncertainty of the prediction

CAN-AI’06, Québec, Canada, 2006 Composition operation A composition  is a set of new plans that are not pre-established satisfying the consistency properties Substitution domain Substitution domain The substitution domain Sub(x) of a variable x   X is a subset Sub(x)     A substitution s is an application s : X ® 2 A, represented by a set of variable-actions pairs: s  { x ¬ a 1, y ¬ a 2  o a 3,…}, where a 1  Sub(x) and a 2 o a 3  Sub(y). Substitution process Substitution process

CAN-AI’06, Québec, Canada, 2006 Recognition space The set of plausible plans ordered by the subsumption relation forms a lattice structure We have   if there is a substitution s  { x ¬ a i, y ¬ b j, …} such that  i  [ 1, |  | ], ( a i  b i )  X  s  a i  s  b i  where  |  | is the cardinality of  = = The upper bound  is the most specific common partial plan subsumer  c r … c 1 , such that  i  1, r  with k  r  min ( n, m )  o j  [ 1, k ],  a i, b i   X  then c j ( o j ), o j  O, a i c i and b i c i. =

CAN-AI’06, Québec, Canada, 2006 Smart Home Validation

CAN-AI’06, Québec, Canada, 2006 First results Simulation of real case scenarios Behavioural recognition accuracy of 47% (Baum et al., 1993)

CAN-AI’06, Québec, Canada, 2006 Conclusion and future work Our formal framework Our formal framework Structure the recognition process Minimize the uncertainty in the predictions Future work Future work Use contextual information and the patient's profile to attribute a probability to plans

CAN-AI’06, Québec, Canada, 2006 Thank you for your attention! Questions? You want to know more…

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