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This paper describes a new algorithm for decision making in two-player real-time video games. As with Monte Carlo Tree Search, the algorithm can be used without heuristics and has been developed for use in general video game AI.
This paper presents the framework, rules, games, controllers, and results of the first General Video Game Playing Competition, held at the IEEE Conference on Computational Intelligence and Games in 2014. The competition proposes the challenge of creating controllers for general video game play, where a single agent must be able to play many different games, some of them unknown to the participants...
The design of algorithms for Game AI agents usually focuses on the single objective of winning, or maximizing a given score. Even if the heuristic that guides the search (for reinforcement learning or evolutionary approaches) is composed of several factors, these typically provide a single numeric value (reward or fitness, respectively) to be optimized. Multi-Objective approaches are an alternative...
This paper highlights an experiment to see how standard Monte Carlo Tree Search handles simple co-operative problems with no prior or provided knowledge. These problems are formed from a simple grid world that has a set of goals, doors and buttons as well as walls that cannot be walked through. Two agents have to reach every goal present on the map. For a door to be open, an agent must be present...
This paper proposes an automatic way of evolving level generators for arbitrary 2D games, which are described in the Video Game Description Language (VGDL). The process works as follows: a game described in VGDL is interpreted and transformed in a set of rules defined in Answer Set Programming (ASP), along with other general and customizable rules. Although a set of rules described in ASP can generate...
General Video Game Playing (GVGP) allows for the fair evaluation of algorithms and agents as it minimizes the ability of an agent to exploit apriori knowledge in the form of game specific heuristics. In this paper we compare four possible combinations of evolutionary learning using Separable Natural Evolution Strategies as our evolutionary algorithm of choice; linear function approximation with Softmax...
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