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Towards a Model of Evolving Agents for 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
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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
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
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
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
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 }
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
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
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
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
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
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