The conventional reinforcement learning approaches have difficulties to handle the policy alternation of the opponents because it may cause dynamic changes of state transition probabilities of which stability is necessary for the learning to converge. This paper presents a method of multi-module reinforcement learning in a multiagent environment, by which the learning agent can adapt itself to ...

In models of learning, it is recognized that the path of play displays some conspicuous patterns when players use simple rules in assessing their opponents' behavior. If the players themselves become aware of such patterns, they may want to utilize them in an attempt to better assess their opponents' behavior. This paper formulates a simple two-person model of learning that allows such pattern ...

Negotiations among autonomous agents has been gained a mass of attention from a variety of communities in the past decade. This paper deals with a prominent type of automated negotiations, namely, multilateral multi-issue negotiation that runs under real-time constraints and in which the negotiating agents have no prior knowledge about their opponents’ preferences over the space of negotiation ...

Behavior in conflict situations can be influenced by the social information that individuals have about their opponents. This paper tests whether an existent Instance-based Learning (IBL) model, built using the Instance-based Learning Theory (IBLT) to explain behavior in a single-person binary-choice task (BCT), can predict behavior in a two-player iterated prisoner’s dilemma (IPD) game. The sa...

We demonstrate how a first-person shooter (FPS) video game can be made more fun and challenging by replacing the hard-wired behavior of opponents with behaviors evolved via an evolutionary algorithm. Using the opensource FPS game Cube as a platform, we replaced the agents' (opponents) hard-wired behavior with binary “DNA” supporting a much richer variety of agent responses. Survival-of-the-fitt...

Unsupervised online learning in commercial computer games allows computer-controlled opponents to adapt to the way the game is being played. As such it provides a mechanism to deal with weaknesses in the game AI and to respond to changes in human player tactics. In prior work we designed a novel technique called “dynamic scripting” that is able to create successful adaptive opponents. However, ...

Firms often lack knowledge of the nature of the uncertainty they or their opponents face and use heuristics or approximations to determine their strategy. We define and analyze one type of "heuristic strategy", in which firms choose strategies based on the expectation of their opponents’ private information rather than the full information about the distribution of that private information. We ...

Neural-Tube Defects, the most common congenital malformation, is closely related to low maternal folate intake. As the Chinese Shanxi Province has one of the highest prevalence rates of Neural-Tube Defects, folate fortification of rice is an excellent alternative to low intake of folate acid pills. This paper analyses the relations between socio-demographic indicators, knowledge, consumer perce...

trolled opponents in video games is called game AI. Adaptive game AI can improve the entertainment value of games by allowing computer-controlled opponents to fix weaknesses automatically in the game AI and to respond to changes in human-player tactics. Dynamic scripting is a reinforcement learning approach to adaptive game AI that learns, during gameplay, which game tactics an opponent should ...

Game AI is the decision-making process of computer-controlled opponents in computer games. Adaptive game AI can improve the entertainment value of computer games. It allows computercontrolled opponents to automatically fix weaknesses in the game AI and respond to changes in human-player tactics. Dynamic scripting is a recently developed approach for adaptive game AI that learns which tactics (i...