1 / 29

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

drago
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related