B. Ross Cosc 4f79 1 Commercial tools Size of system: –small systems 400 rules single user, PC based –larger systems narrow, problem-type specific or hybrid shells using many problem-solving paradigms (rules, induction, NN, GA,...) rules can be multi-user (esp. WWW) Type of system –expert system consultation –decision-support system: high-end analysis of data (data mining)

B. Ross Cosc 4f79 2 Commercial Tools p.93-95

B. Ross Cosc 4f79 3 Commercial Tools

B. Ross Cosc 4f79 4 Commercial Tools

B. Ross Cosc 4f79 5 Commercial Tools Evaluating knowledge engineering tools - Consultation paradigm: diagnosis? planning? configuration?... - AI paradigms: Representation, inference, control, uncertainty, neural nets - Implementation: Lisp, Prolog, C based, speed, transportable, interfacing, compiled code, WWW - User interface: explanation, graphics, windowing, knowledge engineering, - Applications: what applications have been implemented with the system - Support: documentation, training, support services Contemporary shells: - multi-paradigm, PC-based (and up), integrate with std languages and applications (eg. databases)

B. Ross Cosc 4f79 6 M.1 by Tecknowledge Inc. prototyping tool can handle small systems ( rule systems) implemented in Prolog EMYCIN strategy: backchaining with uncertainty, unknown values supports "variable" rules: rule macro's window, menu interface M4: latest version ($1000) - embeds expert system code into applications - VB version embeds into Visual Basic ($199)

B. Ross Cosc 4f79 7 M.1 p. 107

B. Ross Cosc 4f79 8 Flex Bundled expert system toolkit with Quintus Prolog, Macprolog, others multiple paradigms: forward and backward chaining, frames forward-chaining rule selection is flexible, and permits builtin or user-defined algorithms to be used Can use Prolog's inference engine: directly call prolog code "data-driven" programming: frame demons procedural control Macintosh interface primitives fairly rudimentary explanation: must indicate explicitly which rules can be in explanation -- and explanation is text (hybrid systems mean that explanation is a problem)

B. Ross Cosc 4f79 9 Flex

B. Ross Cosc 4f79 10 OPS5, OPS83 OPS5: Carnegie-Mellon Used to implement XCON Production-rule, forward-chaining system Uses time stamps to fire rules (least-recently used strategy) intended for larger expert systems Implemented in Lisp interface permits a programming environment OPS83: successor to OPS5 more generalized rule format, control embeddable in C

B. Ross Cosc 4f79 11 OPS5 p.118

B. Ross Cosc 4f79 12 RuleMaster by Radian Corporation (Texas) and Intelligent Terminals (Scotland) inductive inference system, intended for small to moderate systems modular approach: expert system components encoded in modules (procedures), which hierarchically call one another Can create rules usig ID3 algorithm, or encode if-then rules directly runs on unix or PC-DOS, down-compiles into C or FORTRAN if desired spreadsheet interface for creating example sets example systems: Willard (severe storm forecasting), OIL (oil tank diagnosis)

B. Ross Cosc 4f79 13 ART The Automated Reasoning Tool (by Inference Corporation; now owned by Brightware Inc)) hybrid tool kit for knowledge system development 4 components: - knowledge language - compiler (knowledge language --> Lisp) - applier (inference engine) - development environment Uses a number of inference paradigms, including frames, logical represenations, forward and backward chaining, procedural execution, uncertainty systems range from $8000-$150,000

B. Ross Cosc 4f79 14 ART p.122

B. Ross Cosc 4f79 15 Intellicorp KEE: Knowledge Engineering Environment by IntelliCorp hybrid system, used for number of genetic expert systems uses frames, rules, procedures, backward and forward chaining $60,000 in 1985 (today’s price ???) Kappa-PC: PC Windows shell object-oriented, rule-based GUI environment interface builder $995

B. Ross Cosc 4f79 16 KEE p. 124

B. Ross Cosc 4f79 17 Trends for commercial shells Most run on PC’s or distributed networks of PC’s WWW is a hot area! Web is now a standard interface for production expert systems. Java is becoming an implementation language Highly interactive development environment are the norm Most systems include a library of various tools & technologies –forward & backward rules, rule induction, NN’s, fuzzy, GA’s The difference between decision support environments and expert systems is becoming vague –data mining applications use same tools as expert systems, although they are applied for often different purposes –main dogmatic difference: expert system KB needs human expert, while data mining uses auto techniques on large DB –both are merging for some problem domains New AI technologies will find their way into shells