THERE MAY BE A HUGE NUMBER OF POTENTIAL SOLUTIONS FOR THE USER TO CHOOSE FROM . THE PURPOSE OF AN AUTOMATIC ADVISOR IS TO HELP THE USER ARTICULATE HIS GOALS AND THEN TO GENERATE CANDIDATE SOLUTIONS FOR THE USER TO ACCEPT OR REJECT . THE MAIN COMPUTATIONAL PROBLEM TO BE SOLVED IS ONE OF CONTROLLING THE COOPERATIVE FUNCTIONING OF SEVERAL SOURCES OF KNOWLEDGE , WHICH MAY USE DIFFERENT REPRESENTATIONAL SCHEMES INTERPRETED BY DIFFERENT INFERENCE ENGINES . A PROMISING ARCHITECTURE APPEARS TO BE ONE WHICH CONSIDERS EACH KNOWLEDGE SOURCE TO BE AN INDEPENDENT AGENT , WHICH ONLY COMMUNICATES WITH OTHER SUCH AGENTS VIA A GLOBAL DATA STRUCTURE , UNDER THE SUPERVISION OF A FLEXIBLE SCHEDULER CAPABLE OF REASONING ABOUT CONTROL . IN ADDITION TO ARCHITECTURAL ISSUES , THE PROJECT EMPHASIZES THE IMPORTANCE OF USER MODELLING IN MAN-MACHINE DIALOGUE AND THE USE OF LARGE DATABASES TO PROVIDE AN ADVISOR WITH COMPREHENSIVE INFORMATION .
. SUPPORTING B-TYPE RESEARCH THEMES
B-TYPE PROPOSALS IN SUPPORT OF AREA 3.1.1 ARE INVITED . SOME OF THESE MAY LEAD TO A-TYPE PROJECTS LATER . CANDIDATE TOPICS INCLUDE :
- DIFFERENTIATION , CLASSIFICATION AND EXTENSION OF KNOWLEDGE REPRESENTATIONS FOR VARIOUS DOMAIN CLASSIFICATIONS , PARADIGMS EMPLOYED AND INFERENCE/PROCESSING MODELS USED
- EXPERIMENTAL APPROACHES TO KNOWLEDGE REPRESENTATION , THEIR SPECIFIC RANGES OF APPLICATION AND THEIR INTEGRATION
- INFERENCE TECHNIQUES , ESPECIALLY FOR KR FORMALISMS THAT HAVE NO KNOWN PROOF ALGORITHM
- META-LEVEL REASONING , INCLUDING PROBLEMS OF SELF-REFERENCE AND SYSTEM " SELF-AWARENESS "
- REAL-TIME INFERENCE TECHNIQUES
- TECHNIQUES FOR PLANNING AND PROGRAMME SYNTHESIS
- STRUCTURING OF INFERENCE TECHNIQUES , AND THE ROLE OF META-KNOWLEDGE
- AUTOMATIC ADAPTATION AND ENHANCEMENT
- INFERENCE UNDER CONDITIONS OF CONFLICTING AND UNCERTAIN KNOWLEDGE
- THE IMPACT OF KNOWLEDGE REPRESENTATION ON INFERENCE TECHNIQUES
- DEFAULT AND CLOSED WORLD REASONING
- NON MONOTONIC , AND PROPORTIONAL DYNAMIC LOGIC
- BYPASSING MECHANISMS FOR DEALING WITH INCONSISTENCIES
- REASONING IN TERMS OF PHYSICAL OBJECTS AND IMAGES
- PARALLELISM OF KBS AND ITS REPRESENTATION
NOTE THAT PROPOSALS RELATED TO THIS AREA ARE ALSO INVITED IN AREAS , , 5.5.2 AND 5.4.2 .
3.1.2 . KNOWLEDGE ACQUISITION AND LEARNING TECHNIQUES ( TYPE A )
DESCRIPTION
THE KNOWLEDGE ACQUISITION PROCESS IS COMPLEX AND A MAJOR EFFORT IS REQUIRED IN THIS DIRECTION TO SPEED UP THE DEVELOPMENT OF KBS . IT IS IMPORTANT TO DIFFERENTIATE HERE BETWEEN ( A ) THE KNOWLEDGE EXTRACTION FROM HUMAN EXPERTS TO SET UP A PARTICULAR KBS , ( B ) LEARNING BY THE KBS AND ( C ) THE OPTIMIZATION OF THE KBS . ALTHOUGH INTERRELATED , THEY ARE DISTINCT PROCESSES . THEREFORE IT IS MANDATORY TO DEVELOP CONCEPTS TO SUPPORT THE DEVELOPMENT OF SYSTEMS FOR KNOWLEDGE ELICITATION AND MODELLING . A MULTIDISCIPLINARY APPROACH , WHICH TAKES INTO ACCOUNT COGNITIVE AND PSYCHOLOGICAL ASPECTS , IS NEEDED TO DEVELOP A SET OF TOOLS RANGING FROM KNOWLEDGE EDITORS TO AUTOMATIC KB CONSTRUCTORS . KR TOOLS WILL BE PRESENTED WITH A VALUABLE TEST BED IN THIS AREA . THIS TASK COVERS THE EXTRACTION OR ELICITATION OF KNOWLEDGE