In divergent interest tacit coordination games there is a tradeoff between selecting a solution with a high individual payoff and one which is perceptually more salient to both players, i.e., a focal point. To construct a cognitive model of decision making in such games we need to consider both the social value orientation of the players and the game features. Therefore, the goal of this study was to construct a cognitive model for predicting the probability of selecting a focal point solution in these types of games. Using bootstrap aggregated ensemble of decision trees that was trained on the “bargaining table’ game behavioural data were able to predict when players will select a focal point solution. The binary classification achieved an accuracy level of 85%. The main contribution of the current study is the ability to model players behaviour based on the interaction between different SVOs and game features. This interaction enabled us to gain different insights regarding player’s behaviour. For example, a prosocial player often showed a tendency towards focal point solutions even when their personal gains were lower than that of the co-player. Thus, SVO is not a sufficient model for explaining behaviour in different divergent interest scenarios.

In tacit coordination games people manage to converge on prominent solutions, which are known as focal points. There is still no accepted explanation of how players manage to converge on the same solution. It could be that the limited explanatory power arises from the fact that existing theories rely on pure strategies to describe behaviour. The aim of the current study is to construct a cognitive model that more accurately describes human behaviour in tacit coordination games. To this end we constructed individual strategic profiles that take into account the subjective preferences of individual players regarding the prominent selection rules. Subsequently, the individual profiles were clustered to gain insights regarding different types of coordinators. By using machine learning and statistical methods we were able to demonstrate, for the first time, the relationship between different types of strategic profiles and coordination ability. The results of this study demonstrate the importance of constructing a descriptive behavioural model that may potentially improve prediction of human decision-making in the context of human-machine interaction.

Abstract Adversarial search, or game‐tree search, is a technique for analyzing an adversarial game to determine what moves a player should make in order to win a game. Until recently, lookahead pathology (in which deeper game‐tree search results in worse play) has been thought to be quite rare. We provide an analysis that shows that every game should have some sections that are locally pathological, assuming that both players can potentially win the game. We also modify the minimax algorithm to recognize local pathologies in arbitrary games and cut off search accordingly (shallower search is more effective than deeper search when local pathologies occur). We show experimentally that our modified search procedure avoids local pathologies and consequently provides improved performance, in terms of decision accuracy, when compared with the minimax algorithm. In addition, we provide an experimental evaluation on the African game of Kalah, which shows the improved performances of our suggested error‐minimizing minimax algorithm when there is a large degree of pathology.

Human social preferences have been shown to play an important role in many areas of decision-making. There is evidence from the social science literature that human preferences in interpersonal interactions depend partly on a measurable personality trait called, Social Value Orientation (SVO). Automated agents are often written by humans to serve as their delegates when interacting with other agents. Thus, one might expect an agent’s behaviour to be influenced by the SVO of its human designer. With that in mind, we present the following: first, we explore, discuss and provide a solution to the question of how SVO tests that were designed for humans can be used to evaluate agents’ social preferences. Second, we show that in our example domain there is a medium–high positive correlation between the social preferences of agents and their human designers. Third, we exemplify how the SVO information of the designer can be used to improve the performance of some other agents playing against those agents, and lastly, we develop and exemplify the behavioural signature SVO model which allows us to better predict performances when interactions are repeated and behaviour is adapted.

An automated negotiator is an intelligent agent whose task is to reach the best possible agreement. We explore a novel approach to developing a negotiation strategy, a ‘domain-based approach’. Specifically, we use two domain parameters, reservation value and discount factor, to cluster the domain into different regions, in each of which we employ a heuristic strategy based on the notions of temporal flexibility and bargaining strength. Following the presentation of our cognitive and formal models, we show in an extensive experimental study that an agent based on that approach wins against the top agents of the automated negotiation competition of 2012 and 2013, and attained the second place in 2014.

In this work we suggest a Belief-Desires-Intentions mental model that provides agents with the social competence to capture and reason about their goals with respect to the goals of other agents/humans in the environment. The suggested architectural model would enable the implementation of generic social competent agents that would interact differently towards different groups. We explore the agent’s behavior on the social spectrum by computationally describing the maximum attainable benefit when it belongs to different types of social groups. In addition, as the mental model requires the agent to have an ability to reason about group membership, which we prove to be NP-complete, we present a way to formulate the problem as a constraints satisfaction problem and evaluate possible heuristics to speed-up the search.

There are many situations in which two or more agents (e.g., human or computer decision makers) interact with each other repeatedly in settings that can be modeled as repeated stochastic games. In such situations, each agent's performance may depend greatly on how well it can predict the other agents' preferences and behavior. For use in making such predictions, we adapt and extend the Social Value Orientation (SVO) model from social psychology, which provides a way to measure an agent's preferences for both its own payoffs and those of the other agents.

When constructing a search tree for multiplayer games, there are two basic approaches to propagating the opponents' moves. The first approach, which stems from the MaxN algorithm, assumes each opponent will follow his highest valued heuristic move. In the second approach, the paranoid algorithm, the player prepares for the worst case by assuming the opponents will select the worst move with respect to him. There is no definite answer as to which approach is better, and their main shortcoming is that their strategy is fixed. We therefore suggest the MaxN-paranoid mixture (MP-Mix) algorithm: a multiplayer adversarial search that switches search strategies according to the game situation. The MP-mix algorithm examines the current situation and decides whether the root player should follow the MaxN principle, the paranoid principle, or the newly presented directed offensive principle. To evaluate our new algorithm, we performed extensive experimental evaluation on three multiplayer domains: Hearts, Risk, and Quoridor. In addition, we also introduce the opponent impact (OI) measure, which measures the players' ability to impede their opponents' efforts, and show its relation to the relative performance of the MP-mix strategy. The results show that our MP-mix strategy significantly outperforms MaxN and paranoid in various settings in all three games.

Multiagent research provides an extensive literature on formal Belief-Desire-Intention (BDI) based models describing the notions of teamwork and cooperation, but adversarial and competitive relationships have received very little formal BDI treatment. Moreover, one of the main roles of such models is to serve as design guide-lines for the creation of agents, and while there is work illustrating that role in cooperative interaction, there has been no empirical work done to validate competitive BDI models.