Evolving Agent Model for Ambient Intelligence: Monitoring and Learning Behavior Patterns

1. Towards a Model of Evolving Agentsfor Ambient Intelligence • Stefania Costantini • Dip. Di Informatica • Univ. degli Studi di L’Aquila, Italy Pierangelo Dell’Acqua Dept. of Science and Technology Linköping University, Sweden • Luís Moniz Pereira • Centro de Inteligência Artificial • Universidade Nova de Lisboa, Portugal • Francesca Toni • Dept. of Computing • Imperial College London, UK • ASAmI’07 – April 2007, Newcastle, UK

2. Motivation • Vision: a setting where agents interact with users with the aim of: • training them • monitoring them for ensuring consistence/coherence in user behavior • Accordance with vision of AmI: a digitally augmented environment which is: • omnipresent, and • can observe and supervise the situation at hand • Assumption: agents are able to: • elicit user behavior patterns • learn rules/plans from other agents by imitation and experience

3. imitation learning learning evolution monitoring user agent training • learning • by experience • by imitation • evolution • by EVOLP • training • by reactive rules • monitoring • by temporal-logic-like rules

4. Agent model MPA • Composed of two layers: • 1. base layer PA • interacts with user • updatable to reflect changes of user pattern behavior • 2. meta-layer MPA • relies on meta-knowledge • contains long term objectives about users, expectations, etc • updatable by social interaction with other agents • updates PA PA MPA user PA

5. Temporal logic-like rules • To characterize the monitoring aspects expressible at the meta-control MPA we introduced temporal logic-like rules • Def. A safety formula F takes the form: • K P WHEN C • K P:T WHEN C • where P and C are sentences, T a time-stamp or time-interval, and • K  { always, sometimes, never, eventually }

6. P:T is true at time t iff P is true at t and • - t  T if T is a time-stamp, or • t  T if T is a time-interval • Def. Let T be a time-stamp and F = K P:T a safety formula. • F is true at time t iff: • P:T is true at t whenever K  { always, sometimes, eventually } • P:T is false at t whenever K = never

7. Def. Let T be a time-interval and F = K P:T a safety formula. • F is true iff: • K = always and  t  T. K P is true at t • K = never and  t  T. K P is false at t • K = sometimes and  t  T. K P is true at t • K = eventually and  t  T. K P is true at t and  t2 T. t2 > t implies K P is true at t2

8. User monitoring by learning-by-imitation and evolution • PA   drink, take_medicine drink  not abnormal(drink) take_medicine  not abnormal(take_medicine) User query: can I drink a glass of wine if I have to take medicine ? drink take_medicine MPA PA 

9. MPA • always do(user, A) when goal(G), necessary(G,A) • goal(healthy) • necessary(healthy, take_medicine) abnormal(drink)  not abnormal(take_medicine) abnormal(take_medicine)  not abnormal(drink)   not take_medicine, mandatory(take_medicine) • mandatory(take_medicine) PA

10. Evaluation of learnt rules • MPA • eventually goal(G)  known_conds(C), learnt(Cond) : t illness(user, cold) goal(healthy)  illness(user, X), recover(X) recover(cold)  do(user, take_aspirin) (*) PA user • (*) learnt rule under evaluation • If PA by interaction with user does not confirm recovered health, • then (*) can be deactivated/removed

11. Towards agent societies • Agent interactionSocial interaction • learning via information exchange  learning via social interaction • among agents and consensus • Agent society • proposes socially accepted behavior rules to agents • learns new behavior rules by exploiting social evaluation techniques - agents responsible for information they provide - agents rewarded if positively evaluated by other agents - reputation/trust of proposing agents