25,435 research outputs found
A Parameterisation of Algorithms for Distributed Constraint Optimisation via Potential Games
This paper introduces a parameterisation of learning algorithms for distributed constraint optimisation problems (DCOPs). This parameterisation encompasses many algorithms developed in both the computer science and game theory literatures. It is built on our insight that when formulated as noncooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of algorithms developed in the computer science literature using game theoretic methods. Furthermore, our parameterisation can assist system designers by making the pros and cons of, and the synergies between, the various DCOP algorithm components clear
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Explaining how to play real-time strategy games
Real-time strategy games share many aspects with real situations in domains such as battle planning, air traffic control, and emergency response team management which makes them appealing test-beds for Artificial Intelligence (AI) and machine learning. End-user annotations could help to provide supplemental information for learning algorithms, especially when training data is sparse. This paper presents a formative study to uncover how experienced users explain game play in real-time strategy games. We report the results of our analysis of explanations and discuss their characteristics that could support the design of systems for use by experienced real-time strategy game users in specifying or annotating strategy-oriented behavior
Decision-Making: A Neuroeconomic Perspective
This article introduces and discusses from a philosophical point of view the nascent field of neuroeconomics, which is the study of neural mechanisms involved in decision-making and their economic significance. Following a survey of the ways in which decision-making is usually construed in philosophy, economics and psychology, I review many important findings in neuroeconomics to show that they suggest a revised picture of decision-making and ourselves as choosing agents. Finally, I outline a neuroeconomic account of irrationality
Evolving Aesthetic Maps for a Real Time Strategy Game
ArtĂculo publicado en congreso SEED'2013 (I Spanish Symposium on Entertainment Computing), Septiembre 2013, Madrid.This paper presents a procedural content generator method that have
been able to generate aesthetic maps for a real-time strategy game. The
maps has been characterized based on several of their properties in order
to de ne a similarity function between scenarios. This function has guided
a multi-objective evolution strategy during the process of generating and
evolving scenarios that are similar to other aesthetic maps while being
di erent to a set of non-aesthetic scenarios. The solutions have been
checked using a support-vector machine classi er and a self-organizing
map obtaining successful results (generated maps have been classi ed as
aesthetic maps)
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