1. L. M. Pereira, J. J. Alferes, J. A. Leite Centro de Inteligência Artificial - CENTRIA Universidade Nova de Lisboa, Portugal P. Dell’Acqua Dept. of Science and Technology - ITN Linköping University, Sweden Joint work with: June 12th, 2003 L’Aquila, Italy Updating, preferring and acting in abductive agents

2. Outline Updating agents Agent’s language Preferring in agents Updates plus preferring Architecture Future works

3. Capabilities of our agents We propose an approach to agents that can: - reason and react to the environment (including 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 - prefer among possible choices - abduce hypotheses to explain observations

4. Updating agents Updating agent: a rational, reactive agent that can dynamically change its own knowledge and goals - makes observations - updates its knowledge, reactions and goals - thinks a bit (rational) - selects and executes an action (reactive)

5. Atomic formulae: A objective atoms not A default atoms i:C projects i  C updates Formulae: L i is an atom, an update or a negated update active rule generalized rules Z j is a project integrity constraint false  L 1  L n  Z 1  Z m A  L 1  L n not A  L 1  L n L 1  L n  Z Agent’s language

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

7. money  maria : not work beach  maria : goToBeach travelling  pedro : bookTravel Consider the following active rules in the theory of Maria. Example: active rules

8. A project i:C can take one of the forms: Note that a program can be updated with another program i.e., any rule can be updated. i : ( A  L 1  L n ) i : ( L 1  L n  Z ) i : ( ?- L 1  L n ) i : ( not A  L 1  L n ) i : ( false  L 1  L n  Z 1  Z m ) Projects

9. Knowledge states represent dynamically evolving states of agents’ knowledge. They undergo change due to updates. Given the current knowledge state P s, its successor knowledge state P s+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. Agents’ knowledge states

10. city  not mountain  not beach  not travelling work vacation  not work mountain  not city  not beach  not travelling  money beach  not city  not mountain  not travelling  money travelling  not city  not mountain  not beach  money Let the underlying theory of Maria be: Since the theory has a unique two-valued model: M={city, work} Maria decides to live in the city. Enabling agents to prefer

11. If we add the fact money to the theory of Maria, then the theory has 4 models: M 1 ={city, money, work}M 2 = {mountain, money, work} M 3 = {beach, money, work}M 4 = {travelling, money, work} Therefore, Maria is unable to decide where to live. To select among alternative choices, Maria needs the ability of preferring. Enabling agents to prefer

12. A logic programming framework that combines two distinct forms of reasoning: preferring and updating. Updates create new models, while preferences allow us to select among pre-existing models The priority relation can itself be updated. A language capable of considering sequences of logic programs that result from the consecutive updates of an initial program, where it is possible to define a priority relation among the rules of all successive programs. Updates plus preferences

13. Preferring agent: an agent that is able to prefer beliefs and reactions when several alternatives are possible. Preferring agents Agents can express preferences about their own rules. Preferences are expressed via priority rules. Preferences can be updated, possibly on advice from others.

14. Let < be a binary predicate symbol whose set of constants includes all the generalized rules: r 1 < r 2 means that the rule r 1 is preferred to rule r 2. A priority rule is a generalized rule defining <. A prioritized LP is a set of generalized rules (possibly, priority rules) and integrity constraints. Priority rules

15. (1) city  not mountain  not beach  not travelling (2) work (3) vacation  not work (4) mountain  not city  not beach  not travelling  money (5) beach  not city  not mountain  not travelling  money (6) travelling  not city  not mountain  not beach  money 1<4  work 4<6  vacation 1<5  work 5<6  vacation 1<6  work 6<1  vacation M={city, money, work} If we add money to the theory, then there is a unique model: If work is false, then vacation holds: M 1 ={mountain, money, vacation}M 2 ={beach, money, vacation} Example: a prioritized LP

16. The initial theory of an agent  is a pair (P,R): - P is an prioritized LP. - R is a set of active rules. An updating program is a finite set of updates. Let 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={U 1,…, U s } is a sequence of updating programs. Agent theory

17. (1) city  not mountain  not beach  not travelling (2) work (3) vacation  not work (4) mountain  not city  not beach  not travelling  money (5) beach  not city  not mountain  not travelling  money (6) travelling  not city  not mountain  not beach  mone 1<4  work 4<6  vacation 1<5  work 5<6  vacation 1<6  work 6<1  vacation money  maria : not work beach  maria : goToBeach travelling  pedro : bookTravel Let the initial theory (P,R) of Maria be: U={ } State: 0 Example: happy story

18. (1) city  not mountain  not beach  not travelling (2) work (3) vacation  not work (4) mountain  not city  not beach  not travelling  money (5) beach  not city  not mountain  not travelling  money (6) travelling  not city  not mountain  not beach  mone 1<4  work 4<6  vacation 1<5  work 5<6  vacation 1<6  work 6<1  vacation money  maria : not work beach  maria : goToBeach travelling  pedro : bookTravel At state 0 Maria receives l  money U={ U 1 ={ l  money } } State: 1 Example: happy story

19. (1) city  not mountain  not beach  not travelling (2) work (3) vacation  not work (4) mountain  not city  not beach  not travelling  money (5) beach  not city  not mountain  not travelling  money (6) travelling  not city  not mountain  not beach  mone 1<4  work 4<6  vacation 1<5  work 5<6  vacation 1<6  work 6<1  vacation money  maria : not work beach  maria : goToBeach travelling  pedro : bookTravel State: 2 Then, Maria receives maria  not work U={U 1 ={ l  money }, U 2 ={ maria  not work } } Example: happy story

20. (1) city  not mountain  not beach  not travelling (2) work (3) vacation  not work (4) mountain  not city  not beach  not travelling  money (5) beach  not city  not mountain  not travelling  money (6) travelling  not city  not mountain  not beach  mone 1<4  work 4<6  vacation 1<5  work 5<6  vacation 1<6  work 6<1  vacation money  maria : not work beach  maria : goToBeach travelling  pedro : bookTravel State: 3 Then, Maria receives f  (5<4  vacation) U={U 1 ={ l  money }, U 2 ={ maria  not work }, U 3 ={ f  (5<4  vacation) } } Example: happy story

21. A multi-agent system M={   1 s,…,   n s } at state s 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. Multi-agent systems

22. Rational P Reactive P+R Control Cycle can abduce XSB Prolog cannot abduce InterProlog Java Agent architecture

23. Rational P Reactive P+R C UpdateH projects External Interface ext.project Updates Action Handler int.project Agent architecture’s implementation by Mattias Engberg

24. Fw: logic-based controllers Use of agents as logic-based controllers (joint work with A. Lombardi) - simple artificial world: balloon environment

25. Fw: agent organizational structures Formalize organizational structures for epistemic multi-agent systems (eMAS) - groups, institutions, societies, etc. - norms, regulations, etc. Ongoing implementation of a platform supporting the interactions of our logic-based agents as well as some forms of agent structures (by Mattias Blixt)

26. Fw: user preference information in query answering how to incorporate abduction: abductive preferences leading to conditional answers depending on accepting a preference Use of preference reasoning at query time to facilitate the retrieval of information wrt. users’ interests (joint work with A. Vitória) group preference: how to tackle the problem arising when we have several users query the system together

27. Applications Applications in which our agent technology can have a significant potential to contribute are web applications, e.g. information integration how to integrate data from multiple heterogeneous sources and to provide a uniform interface, e.g. to provide info about movies web-site management self-reconfigurable and adaptive web sites declarative representation of web sites allows: - to automatically reconstruct them, e.g. on usage patterns and can adapt themselves wrt. user profiles - to enforce integrity constraints on web sites, e.g. no dangling pointers