WP03 AI tools: Case Based Reasoning

1. WP03AI tools: Case Based Reasoning Miquel Sànchez-Marrè Knowledge Engineering & Machine Learning Group http://www.lsi.upc.es/webia/keml Dept. Of Computer Software Technical University of Catalonia (UPC)

2. WP03 objectives • Creation of a web-accesible Case-Based Reasoning tool. • Integration of Case-Based Reasoning into the Emergency Management Trainer: • Integration of CBR tools with the primary emergency simulation, that augments the simulation tools with an empirical approach based on similarity and analogy; • Integration of CBR concepts and tools in the didactic components of the system for the analysis of student performance and the selection of illustrative examples for in-depth explanation.

3. DONE In progress WP03 work plan • Creation of the CBR server • D03.1 AI tools: CBR (Implementation report) • D03.2 AI tools: CBR (Prototype implementation) • Connection with the RTXPS server • Introduction of cases in the Case Library

4. CBR: A methodology of solving new problems by adapting the solutions of previous similar problems It uses cases as an episodic memory (Case Library). Case-Based Reasoning (I) Solution New Solution Problem New Problem

5. Case-Based Reasoning (II) • A case is a set of features • case identifier • derivation of the case • description of the problem • diagnostic of the problem • solution to the problem • evaluation of the solution (success/failure) • utility measure • other relevant information

6. CASE LIBRARY Case-Based Reasoning (III) • The CBR cycle: new case Retrieve Learn retrieved cases case to store best case evaluated solution (fail/ success) DOMAIN KNOWLEDGE Adapt adapted solution Eval

7. CASE LIBRARY How is the A-TEAM CBR? (I) CBR module new case emergency Retrieve retrieved cases best case DOMAIN KNOWLEDGE adapted solution Adapt simulation

8. How is the A-TEAM CBR? (II) • Retrieval: • Retrieving cases is more difficult than retrieval in DB. • Retrieval in DB = exact matching • Retrieval in CBR = partial matching (similarity) • Similarity: • is computed among case descriptions, • is usually an evaluation heuristic function or distance, • can be domain-dependent. • A-TEAM approach: • case description = set of attributes • similarity measure = • Retrieval maximises the similarity between the actual case and the retrieved one(s).

9. How is the A-TEAM CBR? (III) • Adaptation: • When the case selected from the Case Lib. does not match perfectly with the new case, the old solution needs to be adapted to the new case solution. • Strategies: • null adaptation • structural adaptation • substitution methods • transformation methods • special-purpose adaptation • derivational adaptation • Adaptation is high case-study dependent.

10. Meta-data case structure in A-TEAM

11. The CBR server: Interactions response (simulation) • Comunication among RTXPS and CBR through HTTP POST Method CBR Server RTXPS Server Simulation Module simulation data request (emergency) off-line on-line

12. CASE LIBRARY The CBR server: parts CBR Server query HTTP request CBR core HTTP server HTTP response solution Http connections Socket connections

13. The CBR server: serving a request (I) HTTP Server HTTP request RTXPS server Web Server HTTP response CBR core POST parameters Results (text) query Java Servlet solution

14. The CBR server: serving a request (II) RTXPS Parameter Descriptors CBR core Case Library Builder Simulated Emergencies Data CASE LIBRARY HTTP server query Case Library Manager solution