Search Heuristic Search vs. Evolutionary Search Prepared by Kirque Leung 18 Mar 05

AI-based creativity research  Knowledge-based systems incorporation of expert knowledge in some domains, usually in the form of rules incorporation of expert knowledge in some domains, usually in the form of rules  Grammars alternative way of representing knowledge in a particular domain  grammar vs. narrative alternative way of representing knowledge in a particular domain  grammar vs. narrative grammars embody rules about languages  their role in creativity stems from viewing designs or compositions as statements in a language grammars embody rules about languages  their role in creativity stems from viewing designs or compositions as statements in a language it concerns the attempt to computationally understand natural languages, or translate between them it concerns the attempt to computationally understand natural languages, or translate between them

Con’t  Search The long journey (made speedier via computation) through an immersive space of possibilities in search of something suitable  production of space (journey vs. computation) The long journey (made speedier via computation) through an immersive space of possibilities in search of something suitable  production of space (journey vs. computation) 2 kinds of search: Heuristic search and evolutionary search 2 kinds of search: Heuristic search and evolutionary search

Heuristic Search  A solution, perhaps a design, a schedule or so forth is constructed gradually, bit by bit, with heuristics (rules of thumb) employed to decide ho to choose each successive part  Heuristics are used to decide which link to explore next  Pretty fuzzy about the next move, it concentrates on exploring areas that are sanctioned by the heuristics in used

Evolutionary Search (Computation)  The use of search algorithm  a computational problem is defined in terms of a search space, usually viewed as a massive collection of potential solutions to the problem (The process of search = task of navigating that space) (The process of search = task of navigating that space)

Con’t  Points closer together in space will also tend to be close in terms of quality, and qualities are derived from the search parameters  one move = change in 1 parameter distance in space is related to distance in terms of parameter settings distance in space is related to distance in terms of parameter settings Idea of close-ness Idea of close-ness  ES makes use of previously visited solutions to help decide where to look next

Con’t  ES doesn ’ t work with 1 solution at a time but a large collection of population of solution at once  local search  ES doesn ’ t allow evolution but it does show some emergent properties  Better solutions are allowed to have “ child ”, and the worse ones to “ die ”  imitation of the nature Population control  optimization (search for the best) Population control  optimization (search for the best)  ES searches the space in parallel

Con’t  In non-euclidean geometry, objects of smaller size on the tableau do not represent a further distance from us, but showing a sense of spatial interiority