1. Towards a Model of Evolving Agents for Ambient Intelligence Pierangelo Dell’Acqua Dept. of Science and Technology Linköping University, Sweden ASAmI’07 – April 2007, Newcastle, UK Stefania Costantini Dip. Di Informatica Univ. degli Studi di L’Aquila, Italy Luís Moniz Pereira Centro de Inteligência Artificial Universidade Nova de Lisboa, Portugal Francesca Toni Dept. of Computing Imperial College London, 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. useragent training monitoring learning evolution training by reactive rules monitoring by temporal-logic-like rules learning by experience by imitation evolution by EVOLP imitation learning

4. userPA MPA PA MPA Agent model 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

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  t 2  T. t 2 > t implies K P is true at t 2

8. User monitoring by learning-by-imitation and evolution  MPA 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

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 PA user (*) learnt rule under evaluation If PA by interaction with user does not confirm recovered health, then (*) can be deactivated/removed 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) (*)

11. Towards agent societies Agent interactionSocial interaction learning via information exchange  learning via social interaction among agentsand 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