In competitive domains, some knowledge about the opponent can give players a clear advantage. This idea led many people to propose approaches to acquire models of opponents, based only on the observation of their input-output behavior. If opponent outputs could be accessed directly, a model can be constructed by feeding a machine learning method with traces of the behavior of the opponent. Howe...

Negotiation is a process in which parties interact to settle a mutual concern to improve their status quo. Traditionally, negotiation is a necessary, but time-consuming and expensive activity. Therefore, in the last two decades, there has been a growing interest in the automation of negotiation. One of the key challenges for a successful negotiation is that usually only limited information is a...

This paper studies automated bilateral negotiation among self-interested agents in complex application domains which consist of multiple issues and real-time constraints and where the agents have no prior knowledge about their opponents’ preferences and strategies. We describe a novel negotiation approach called OMAC (standing for “Opponent Modeling and Adaptive Concession”) which combines effi...

This paper exhibits the transformation of Watkins’ Q(λ) learning algorithm into an adversarial Qlearning algorithm. A method called context-based prediction, borrowed from multimedia data coding, is used as opponent modeling and is incorporated in the transformed, CBQ(λ) algorithm. We tested CBQ(λ) by playing against three opponents. The first opponent had no prior knowledge and discovered poli...

Much of the work on opponent modeling for game tree search has been unsuccessful. In two-player, zero-sum games, the gains from opponent modeling are often outweighed by the cost of modeling. Opponent modeling solutions simply cannot search as deep as the highly optimized minimax search with alpha-beta pruning. Recent work has begun to look at the need for opponent modeling in n-player or gener...

In simple dyadic games such as rock, paper, scissors (RPS), people exhibit peculiar sequential dependencies across repeated interactions with a stable opponent. These regularities seem to arise from mutually adversarial process of trying outwit their What underlies this process, and what are its limits? Here, we offer novel framework for formally describing quantifying human reasoning in the ga...

Opponent modeling is a skill in multi-agent systems (MAS) which attempts to create a model of the behavior of the opponent. This model can be used to predict the future actions of the opponent and generate appropriate strategies to play against it. Several researches present different methods to create an opponent model in the RoboCup environment. However, how these models can impact the perfor...

Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent model which is robust to the observation noise existing due to the fog of war. In order to cope...