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João Alexandre Leite Luís Moniz Pereira Centro de Inteligência Artificial - CENTRIA

Evolving Multi-Agent Viewpoints. - An Architecture. Pierangelo Dell’Acqua Dept. of Science and Technology Linköping University pier@itn.liu.se. João Alexandre Leite Luís Moniz Pereira Centro de Inteligência Artificial - CENTRIA Universidade Nova de Lisboa

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João Alexandre Leite Luís Moniz Pereira Centro de Inteligência Artificial - CENTRIA

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  1. Evolving Multi-Agent Viewpoints - An Architecture Pierangelo Dell’Acqua Dept. of Science and Technology Linköping University pier@itn.liu.se João Alexandre Leite Luís Moniz Pereira Centro de Inteligência Artificial - CENTRIA Universidade Nova de Lisboa { jleite, lmp }@di.fct.unl.pt Porto, 17-20 Dec. 2001 Epia01

  2. Our agents We propose a LP approach to agents that can: • ReasonandReact to other agents • Update their own knowledge, reactions and goals • Interact by updating the theory of another agent • Decide whether to accept an update depending on the requesting agent • Capture the representation of social evolution

  3. Framework This framework builds on the work: • Updating Agents P. Dell’Acqua & L. M. Pereira - MAS’99 • Multi-dimensional Dynamic Logic Programming L. A. Leite & J. J. Alferes & L. M. Pereira - CLIMA’01

  4. Updating agents • Updating agent:a rational, reactive agent that can dynamically change its own knowledge and goals • makes observations • reciprocally updates other agents with goals and rules • thinks (rational) • selects and executes an action (reactive)

  5. Multi-Dimensional Logic Programming • In MDLP knowledge is given by a set of programs. • Each program represents a different piece of updating knowledge assigned to a state. • States are organized by a DAG (Directed Acyclic Graph) representing their precedence relation. • MDLP determines the composite semantics at each state according to the DAG paths. • MDLP allows for combining knowledge updates that evolve along multiple dimensions.

  6. Contribution • To extend the framework of MDLP with integrity constraints and active rules. • To incorporate the framework of MDLP into a multi-agent architecture. • To make the DAG of each agent updatable.

  7. DAG A directed acyclic graph DAGis a pair D=(V,E) where V is a set of vertices and E is a set of directed edges.

  8. Agent’s language Atomic formulae: Aobjective atoms not Adefault atoms i:Cprojects iC updates Formulae: generalized rules Li is an atom, an update or a negated update A ¬ L1 Ù...Ù Ln not A ¬ L1 Ù...Ù Ln Zj is a project integrity constraint false ¬ L1 Ù...Ù Ln Ù Z1 Ù...Ù Zm active rule L1 Ù...Ù Ln  Z

  9. Projects and updates A projectj:Cdenotes the intention of some agent i of proposing the updating the theory of agent j with C. iCdenotes an update proposed by i of the current theory of some agent j with C. fredC wilma:C

  10. Agents’ knowledge states • Knowledge states represent dynamically evolving states of agents’ knowledge. They undergo change due to updates. • Given the current knowledge state Ps , its successor knowledge state Ps+1 is produced as a result of the occurrence of a set of parallel updates. • Update actions do not modify the current or any of the previous knowledge states. They only affect the successor state: the precondition of the action is evaluated in the current state and the postcondition updates the successor state.

  11. Agent’s language A projecti:Ccan take one of the forms: i:( A ¬ L1 Ù...Ù Ln ) i:( not A ¬ L1 Ù...Ù Ln ) i:( false ¬ L1 Ù...Ù Ln Ù Z1 Ù...Ù Zm ) i:( L1 Ù...Ù Ln  Z ) i:( ?- L1 Ù...Ù Ln ) i:edge(u,v) i:not edge(u,v)

  12. Initial theory of an agent A multi-dimensional abductive LP for an agent  is a tuple: T =  D, PD, A, RD - D=(V,E) is a DAG s.t. ´V (inspection point of ). - PD={PV | vV} is a set of generalized LPs. - A is a set of atoms (abducibles). - RD={RV | vV} is a set of set of active rules.

  13. The agent’s cycle • Every agent can be thought of as an abductive LP equipped with a set of inputs represented as updates. • The abducibles are (names of) actions to be executed as well as explanations of observations made. • Updates can be used to solve the goals of the agent as well as to trigger new goals.

  14. alfredo´ judge mother father alfredo girlfriend state 0 Happy story - example DAG of Alfredo inspection point of Alfredo The goal of Alfredo is to be happy

  15. Happy story - example alfredo´ judge hasGirlfriend ¬ not happy  father:(?-happy) not happy  mother:(?-happy) getMarried Ù hasGirlfriend  girlfriend:propose moveOut  alfredo:rentApartment custody(judge,mother)  alfredo:edge(father,mother) {moveOut, getMarried} mother father alfredo girlfriend abducibles state 0

  16. Happy story - example alfredo´ judge hasGirlfriend ¬ not happy  father:(?-happy) not happy  mother:(?-happy) getMarried Ù hasGirlfriend  girlfriend:propose moveOut  alfredo:rentApartment custody(judge,mother)  alfredo:edge(father,mother) {moveOut, getMarried} mother father alfredo girlfriend state 0

  17. Agent theory The initialtheoryof an agent  is a multi-dimensional abductive LP. Let an updating programbe a finite set of updates, and S be a set of natural numbers. We call the elements sS states. An agent  at state s, written s , is a pair (T,U): - T is the initial theory of . - U={U1,…, Us} is a sequence of updating programs.

  18. Multi-agent system A multi-agent systemM={1s ,…, ns }at states is a set of agents 1,…,n at state s. M characterizes a fixed society of evolving agents. The declarative semantics of M characterizes the relationship among the agents in M, and how the system evolves. The declarative semantics is stable models based.

  19. Happy story - 1st scenario Suppose that at state 1, Alfredo receives from the mother: mother(happy ¬ moveOut) mother(false ¬ moveOut Ù not getMarried) mother(false¬nothappy) and from the father: father(happy ¬ moveOut) father(not happy ¬ getMarried)

  20. Happy story - 1st scenario alfredo´ false¬nothappy happy ¬ moveOut false ¬ moveOut Ù not getMarried judge mother father happy ¬ moveOut not happy ¬ getMarried alfredo In this scenario, Alfredo cannot achieve his goal without producing a contradiction. Not being able to make a decision, Alfredo is not reactive at all. girlfriend state 1

  21. Happy story - 2nd scenario Suppose that at state 1 Alfredo’s parents decide to get divorced, and the judge gives custodity to the mother. judgecustody(judge,mother)

  22. Happy story - 2nd scenario alfredo´ custody(judge,mother) judge hasGirlfriend ¬ not happy  father:(?-happy) not happy  mother:(?-happy) getMarried Ù hasGirlfriend  girlfriend:propose moveOut  alfredo:rentApartment custody(judge,mother)  alfredo:edge(father,mother) mother father alfredo girlfriend state 1

  23. Happy story - 2nd scenario alfredo´ Note that the internal update produces a change in the DAG of Alfredo. judge mother father Suppose that when asked by Alfredo, the parents reply in the same way as in the 1st scenario. alfredo girlfriend state 2

  24. Happy story - 2nd scenario alfredo´ false¬nothappy happy ¬ moveOut false ¬ moveOut Ù not getMarried judge mother father happy ¬ moveOut not happy ¬ getMarried alfredo Now, the advice of the mother prevails over and rejects that of his father. girlfriend state 2

  25. Happy story - 2nd scenario alfredo´ Thus, Alfredo gets married, rents an apartment, moves out and lives happily ever after. judge hasGirlfriend ¬ not happy  father:(?-happy) not happy  mother:(?-happy) getMarried Ù hasGirlfriend  girlfriend:propose moveOut  alfredo:rentApartment custody(judge,mother)  alfredo:edge(father,mother) mother father alfredo girlfriend state 2

  26. Syntactical transformation The semantics of an agent  at state s, s=(T,U), is established by a syntactical transformation  that maps s into an abductive LP:  s = P,A,R 1.s P´,A,R P´ is a normal LP, A and R are a set of abducibles and active rules. 2.Default negation can then be removed from P´ via the abdual transformation (Alferes et al. ICLP99): P´  P P is a definite LP.

  27. Agent architecture  s = P,A,R Java CC InterProlog (Declarativa) InterProlog (Declarativa) Rational P Reactive P+R can abduce cannot abduce XSB Prolog XSB Prolog

  28. ActionH External Interface UpdateH ext.project Updates int.project CC projects Rational P Reactive P+R Agent architecture  s = P,A,R

  29. Future work • At the agent level: • How to combine logical theories of agents expressed over graph structures. • How to incorporate other rational abilities, e.g., learning. • At the multi-agent system level: • Non synchronous, dynamic multi-agent system. • How to formalize dynamic societies of agents. • How to formalize the notion of organisational reflection.

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