1. Answer Set Programming for Information Agents Vienna University of Technology Knowledge-Based Systems Group Thomas Eiter and Michael Fink

2. Answer Set Programming Recent development in nonmonotonic reasoning Use (non-monotonic) logic programs for declarative problem solving Method:  Represent the problem, P, by an (extended) logic program,   Compute some / all answer sets AS(  ) of   Extract some / all solutions S of P from some / all sets A in AS(  Transformations P ->  A -> S efficient

3. ASP (2) Related: Problem reduction to SAT solvers (chaff,…). Difference to Prolog:  purely declarative: rule order, subgoal order irrelevant  nonmonotonic negation (also unstratified)  nondeterminism  Efficient ASP solvers are available (Smodels, DLV, ASSAT)  Useful tools / reasoning engines for domain-specific KR formalisms and problems

4. Information Agents Crucial in emerging (global) information systems. Cooperation within societies of agents Some kinds of agents:  Facilitators: control sub-agents and coordinate services  Brokers: Match between data sources / services and user requests  Mediators: Exploit meta-knowledge about provider agents to create higher-level services

5. Information Agents (2) Further infrastructure:  Yellow pages: info + matchmaking  Blackboards Further auxiliary agents:  Web-Crawlers  Info-Raiders ...

6. Information Agents (3) Desire: “Intelligent” Agents Need: Rational Capabilities  inferences (deduction, abduction, …)  plausible conclusions  deal with incomplete / unsure / unreliable information Exploit nonmonotonic formalisms & logics Build task-tailored reasoning components

7. Information Agents (4) Prototypical Architecture:

8. Information Agents (5) Problems & Challenges:  decompose query requests  integrate query request & user profile  select information source  create & execute a query plan  compose / merge query answers  data cleaning  data integration: detect / resolve inconsistencies All using  (rich) domain knowledge  meta-knowledge about sources

9. Example Site Selection Task: Given a user query, select most relevant source. Requires background knowledge (about application domain, information sources), nonmonotonic reasoning due to incomplete information, declarative semantics diserable.  use ASP [Eiter et al., KR’02]. Site-Selection Capability User Agent Information Agent XML Site1 XML Site3 XML Wrapper Site2 Query (XML-QL) Query (XML-QL)

10. FUNCTION HCMovies($MovieDB:"movie.dtd"){ CONSTRUCT { WHERE $t "Alfred" "Hitchcock" IN source($MovieDB) CONSTRUCT $t } } Example Movie Domain: information sources s 1, s 2, s 3. Which movies are directed by Alfred Hitchcock? Known: s 1 good for directors, s 2 for person data, s 3 not reliable. Expected: select s 1.

11. General Architecture Query Description  qd : Abstract representation of query. Domain Theory  dom : Domain specific background knowledge. Site Description  sd : Information about the sources. Site-Selection Program  sel : Qualitative and quantitative selection rules and constraints; user preferences.  sd  dom R(Q)   qd parsing Q Selected Site  sel, < u  Q, <

12. Abstract Query Description Based on a general view of a query consisting of a construct part, a where part, and a source part. Generated from a set of elementary facts R(Q) by application of program  qd. Relevant items identified with context-reference pairs (C,P), e.g., access(O,C,P,Q). High-level description predicates: query(Q), access(O,C,P,Q), occurs(O,V), selects(O,C,V), constructs(O,C,P), joins(O 1,O 2,C).

13. A site-selection program  sel for the movie domain: Core rules  sel : r 1 : query_site(s 2,Q)  default_object(O,”Person”,Q) ; r 2 : query_site(s 1,Q)  selects(O,equal,”Hitchcock”), access(O,”Director”,”Personalia/LastName”,Q) ; r 3 : query_site(S,Q)  default_path(O,”LastName”,Q), default_object(O,T,Q), accurate(S,T,high) ; Example Program c

14. Auxiliary rules  sel : r 4 : high_acc(T,Q)  access(O,T,P,Q), accurate(S,T,high) ; r 5 : high_cov(T,Q)  access(O,T,P,Q), covers(S,T,high) ; Optimization rules  sel : c 1 :  query_site(S,Q), high_acc(T,Q), not accurate(S,T,high) [10:1] ; c 2 :  query_site(S,Q), high_cov(T,Q), not covers(S,T,high) [5:1] ; User preferences < u : n r 1 (Q,_) < u n r 3 (Q,_,_,_). Example Program (ctd.) o aux

15. Application Implemented on top of dlv [Eiter et al. 1998] and its front end plp [Delgrande, Schaub, Tompits 2001]. Agentized in IMPACT [Subrahmanian et al. 2000]. Experimental site selection environment movie domain:  Modeled DTD from a set of relevant movie concepts captured by the Open Directory Project.  Wrapped parts of the Internet Movie Database (IMDb) and the EachMovie Database to XML; created 6 different databases.

16. Application (ctd.) Movie databases:  RandomMovies (RM),  RandomPersons (RP),  EachMovie (EM),  Hitchcock (HC),  KellyGrant (KG),  Horror60 (H60). Site descriptions: contents, quality, cost, reliability,etc. Domain knowledge: simple ontological knowledge from the DTD + some background knowledge. Site selection program: (several pages of code).

17. Experimental Queries Formulated a number of natural user queries, including: Q 1 : Which movies were directed by Alfred Hitchcock? Q 2 : In which movies directed by Josef von Sternberg acted Marlene Dietrich? Q 3 : In which year has the movie “Arsenic and Old Lace” been released? Q 4 : In which movies directed by Alfred Hitchcock acted Marlene Dietrich? Q 5 : In which film noirs did Marilyn Monroe act? Modified selection base: adding/removing databases and their descriptions.

18. Results Selection results satisfactory and explainable: Specific core site selection rules trigger. Domain knowledge identifies irrelevant or specific sites by ontological reasoning, reasoning over genre info. etc, Quantitative selection in case of equally preferred AS. QueryQ1Q1 Q2Q2 Q3Q3 Q4Q4 Q5Q5 Candidates HCRM, HC, H60, KG KGHCRM, HC, KG Best HCRMKGHCRM

19. Conclusion ASP is a new problem solving paradigm applicable for many problems, useful and promising for information agents. IMPACT agents: reasonable status set semantics = answer set semantics. Problem specific reasoning components on top of ASP, e.g., site selection. Future work:  Tackle further problems of information agents (or related fields) where ASP approaches are promising.  Coupling of approaches with existing tools, e.g., for learning, planning, ontological reasoning, etc.