A Genetic Programming Framework for 2D Platform AI

Genetic Programming, GP, Game AI, Agent Design, Platformer, AISB, JGAP, platformerAI, symbolic learningThere currently exists a wide range of techniques to model and evolve artificial players for games. Existing techniques range from black box neural networks to entirely hand-designed solutions. In this paper, we demonstrate the feasibility of a genetic programming framework using human controller input to derive meaningful artificial players which can, later on, be optimised by hand. The current state of the art in game character design relies heavily on human designers to manually create and edit scripts and rules for game characters. To address this manual editing bottleneck, current computational intelligence techniques approach the issue with fully autonomous character generators, replacing most of the design process using black box solutions such as neural networks or the like. Our GP approach to this problem creates character controllers which can be further authored and developed by a designer it also offers designers to included their play style without the need to use a programming language. This keeps the designer in the loop while reducing repetitive manual labour. Our system also provides insights into how players express themselves in games and into deriving appropriate models for representing those insights. We present our framework, supporting findings and open challenges

A Genetic Programming Framework for 2D Platform AI

There currently exists a wide range of techniques to model and evolve artificial players for games. Existing techniques range from black box neural networks to entirely hand-designed solutions. In this paper, we demonstrate the feasibility of a genetic programming framework using human controller input to derive meaningful artificial players which can, later on, be optimised by hand. The current state of the art in game character design relies heavily on human designers to manually create and edit scripts and rules for game characters. To address this manual editing bottleneck, current computational intelligence techniques approach the issue with fully autonomous character generators, replacing most of the design process using black box solutions such as neural networks or the like. Our GP approach to this problem creates character controllers which can be further authored and developed by a designer it also offers designers to included their play style without the need to use a programming language. This keeps the designer in the loop while reducing repetitive manual labour. Our system also provides insights into how players express themselves in games and into deriving appropriate models for representing those insights. We present our framework, supporting findings and open challenges

Comparing question answering strategies for Cluedo

The game of Cluedo – also known as Clue – requires working out a ‘murder’ scene by elimination. Beginners typically rely only on cards in their hand and cards they have seen; experts also use propositional logic about cards they have not seen, based on questions asked and answers given.  A game-playing program has been written to test the value of using deductions to guide question-asking. This paper describes how the program has been designed and presents results for five strategies (including a ‘no intelligence’ strategy) for three player games and six player games. The program has been written using JESS (the Java Expert System Shell).  The results were not quite as expected. Using propositional logic did indeed allow the game to be solved in fewer turns, but there were times when adding extra information to the logical deductions made things worse, not better. There is also a strong effect from the mechanics of the game – specifically, which room is chosen as the ‘guilty’ location – on the number of turns required to solve the problem.  It is suggested that strategies might benefit from occasionally breaking away from their highly focussed approach to inject variety into the questioning  The test cases used are listed in an appendix