1. MINERVA A Dynamic Logic Programming Agent Architecture João Alexandre Leite José Júlio Alferes Luís Moniz Pereira ATAL’01 CENTRIA – New University of Lisbon Portugal 02/August/2001 – 10:30

2. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'012 Motivation Logic Programming + Nonmonotonic Reasoning –Precise semantics –Directly implementable (SMODELS & XSB) Combine LPNMR capabilities –Abduction (planning, belief revision,...) –Induction (learning)...within the Agency Concept Cross fertilization between LPNMR & Agents

3. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'013 Traditional LP Agents Knowledge Base –Fixed intensional part (rules) –Variable extensional part (fluents) Inference Mechanism –Top-down goal based –Bottom-up situation based –Combination of both

4. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'014 LP Agents for Dynamic Worlds Nowadays, most work concerns the design of specialized agents; Should not stop investigating more general forms of agency; Use learnt knowledge in new scenarios, i.e. allow the intensional KB to be updated; Dynamic Logic Programming.

5. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'015 Dynamic Logic Programming Knowledge at each state is given by a (generalized) logic program; DLP applies the principle of inertia to rules, ensuring the temporal precedence relation; DLP assigns a (declarative) meaning to such sequences of logic programs; Transformational Semantics allows for a direct implementation.

6. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'016 LUPS - Language of Updates DLP assings a meaning to sequences of logic programs; DLP does not specify such LPs How do we come up with such LPs? –Allow additions and retractions of rules –Rules may represent (persistent) laws –Updates rules may be conditional on present state LUPS is a declarative language that allows a flexible specification of the logic program “at the next state”

7. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'017 LUPS - Commands assert[event](L 0  L 1,…, L n ) when (C 1,…, C n ) retract[event](L 0  L 1,…, L n ) when (C 1,…, C n ) always[event](L 0  L 1,…, L n ) when (C 1,…, C n ) cancel(L 0  L 1,…, L n ) when (C 1,…, C n )

8. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'018 LUPS – Action Specification Simple effect of actions Complex effect of actions “  1 or  2 causes L 1 if F 1,...,F k ” “  1 and  2 causes L 2 if F 1,...,F k ” always L 1 when F 1,...,F k,  1,not  2 always L 1 when F 1,...,F k,not  1,  2 always L 2 when F 1,...,F k,  1,  2 Epistemic effect of actions Non-deterministic effect of actions

9. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'019 Multi-dimensional Dynamic Logic Programming States in DLP can represent other hierarchically related aspects of a system, such as social relations. No multiple inheritance. But DLP cannot cope with more than one such aspect. MDLP generalizes DLP to allow states organized according to acyclic directed graphs.

10. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'0110 Social Representation with MDLP

11. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'0111 Architecture

12. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'0112 Sub-agents (example) assert goal(G,T,A)@goals when MsgFrom(A,G,T,_),coop(A) assert Rule@Agent when MsgFrom(Agent,_,_,Rule) assert MsgTo(A,G,plan(G,P,C),C)@reactions when goal(G,T,A)@goals,A  , plan(G,P,C)@plans

13. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'0113 Sub-agents (example) assert event  @reactions when L 1,…, L n assert event runaway@reactions when danger assert event not  @reactions when L 1,…, L n

14. MINERVA – A Dynamic Logic Programming Agent Architecture 02/08/2001 - 10:30ATAL'0114 Concluding Remarks Multi-Dimensional Dynamic Logic Programming to represent: –Temporal evolution –Social Relationships LUPS to represent: –State Transitions Enjoys the advantages of Logic Programming Good framework to study the combination of several NMR reasoning mechanisms