1. Some Thoughts to Consider 9 How could it possibly be the case that using AI techniques to develop a knowledge base is easier than just programming the solution to a problem? What happens when the amount of knowledge required to solve a problem is very large? So, what would it be like to represent enough knowledge for the Web to really be a ‘Semantic Web’?

2. Special Hardcopy Handout “Building a Small Knowledge System” from Harmon and King, Expert Systems: Artificial Intelligence in Business. The paper details the importance of and the methodology for developing such systems. The basic idea is that very useful, but relatively small, commercial systems can be built using AI tools and techniques - The knowledge bases don’t necessarily have to be extremely large or have to contain lots of comprehesive human expertise in order to be useful. The example developed is a ‘Media Advisor’, implemented as a rule-based system.

3. Managing a Knowledge Based Development Project Start with a very narrowly-focused problem domain. Keep the team small - 3 to 4 people. Find a management champion that believes in the new way of going about the problem. Find a single expert who possesses most of the knowledge relative to the problem. Have a high tolerance for ambiguity. Every month perform a go/no go evaluation of the progress. Cancel or realign the project if necessary. Have lots of knowledge-focused meetings involving the team and expert. Create storyboards, scenarios, use cases, query cases. Focus on the knowledge. Don’t commit to implementation details too early. But do use tools to visualize, simulate, and prototype the knowledge flow. When the team has familiarity with all the knowledge, select appropriate representation and implementation strategies and tools. Continue developing to production using Agile Development methods. Perform an extensive postmortem analysis of what went well and what could have been done differently.

4. A Whodunnit Logic Example person(allan, 25, m, football_player). person(allan, 25, m, butcher). person(barbara, 22, f, hairdresser). person(bert, 55, m, carpenter). person(john, 25, m, pickpocket). had_affair(barbara, john). had_affair(barbara, bert). had_affair(susan, john). killed_with(susan, club). motive(money). motive(jealousy). smeared_in(catherine, blood). smeared_in(allan, mud). owns(bert, wooden_leg). owns(john, pistol). operates_identically(wooden_leg, club). operates_identically(bar, club). operates_identically(pair_of_scissors, knife). operates_identically(football_boot, club). owns_probably(X, football_boot) :- person(X, _, _, football_player). owns_probably(X, pair_of_scissors) :- person(X, _, _, _, _). owns_probably(X, Object) :- owns(X, Object). suspect(X) :- killed_with(susan, Weapon), operates_identically(Object, Weapon), owns_probably(X, Object). suspect(X) :- motive(jealousy), person(X, _, m, _), had_affair(susan, X). suspect(X) :- motive(jealousy), person(X, _, f, _), had_affair(X, Man), had_affair(susan, Man). suspect(X) :- motive(money), person(X, _, _, pickpocket).

5. Assignment 4 Due: November 18 Part 1: Take the Whodunnit Prolog logic example and reverse engineer the logic by writing a story in English prose that explains what is going on. Try to use all the facts and inferences present in the logic. Part 2: Construct a semantic network representation of the Whodunnit example. It should be constructed in a manner that facilitates reasoning by a crime-solving agent.