Publicación: TEACH ME TO PLAY, GAMER! IMITATIVE LEARNING IN COMPUTER GAMES VIA LINGUISTIC DESCRIPTION OF COMPLEX PHENOMENA AND DECISION TREES
Fecha
2022
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SOFT COMPUTING
Resumen
IN THIS ARTICLE, WE PRESENT A NEW MACHINE LEARNING MODEL BY IMITATION BASED ON THE LINGUISTIC DESCRIPTION OF COMPLEX PHENOMENA. THE IDEA CONSISTS OF, FIRST, CAPTURING THE BEHAVIOUR OF HUMAN PLAYERS BY CREATING A COMPUTATIONAL PERCEPTION NETWORK BASED ON THE EXECUTION TRACES OF THE GAMES AND, SECOND, REPRESENTING IT USING FUZZY LOGIC (LINGUISTIC VARIABLES AND IF-THEN RULES). FROM THIS KNOWLEDGE, A SET OF DATA (DATASET) IS AUTOMATICALLY CREATED TO GENERATE A LEARNING MODEL BASED ON DECISION TREES.
THIS MODEL WILL BE USED LATER TO AUTOMATICALLY CONTROL THE MOVEMENTS OF A BOT. THE RESULT IS AN ARTIFICIAL AGENT THAT MIMICS THE HUMAN PLAYER. WE HAVE IMPLEMENTED, TESTED AND EVALUATED THIS TECHNOLOGY FROM TWO DIFFERENT POINTS OF VIEW: PERFORMANCE BY USING CLASSICAL METRICS (ACCURACY, ROC AREA AND PRC AREA) AND BELIEVABILITY BY USING A TURING TEST FOR TRAINED BOTS. THE RESULTS OBTAINED ARE INTERESTING AND PROMISING, SHOWING THAT THIS METHOD CAN BE A GOOD ALTERNATIVE TO DESIGN AND IMPLEMENT THE BEHAVIOUR OF INTELLIGENT AGENTS IN VIDEO GAME DEVELOPMENT.
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MACHINE LEARNING, LINGUISTIC DESCRIPTION OF COMPLEX PHENOMENA, INTELLIGENCE AGENTS, IMITATIVE LEARNING, COMPUTER GAMES
