Hypergame Analysis in E-Commerce: A Preliminary Report

In usual game theory, it is normally assumed that "all the players see the same game", i.e., they are aware of each other's strategies and preferences. This assumption is very strong for real life where differences in perception affecting the decision making process seem to be the rule rather the exception. Attempts have been made to incorporate misperceptions of various types, but most of these attempts are based on quantities (as probabilities, risk factors, etc.) which are too subjective in general. One approach that seems to be very attractive is to consider that the players are trying to play "different games" in a hypergame. In this paper, we present a hypergame approach as an analysis tool in the context of multiagent environments. Precisely, we first sketch a brief formal introduction to hypergames. Then we explain how agents can interact through communication or through a mediator when they have different views and particularly misperceptions on others' games. After that, we show how agents can take advantage of misperceptions. Finally, we conclude and present some future work. Dans les jeux classiques, il est supposé que "tous les joueurs voient le même jeu'', i.e., que les joueurs sont au courant des stratégies et des préférences des uns et des autres. Aux vu des applications réelles, cette supposition est très forte dans la mesure où les différences de perception affectant la prise de décision semblent plus relevées de la règle que de l'exception. Des tentatives ont été faites, par le passé, pour incorporer les distorsions aux niveaux des perceptions, mais la plupart de ces tentatives ont été essentiellement basées sur le "quantitatif" (comme les probabilités, les facteurs de risques, etc.) et par conséquent, trop subjectives en général. Une approche qui semble être attractive pour pallier à cela, consiste à voir les joueurs comme jouant "différents jeux'' dans une sorte d'hyper-jeu. Dans ce papier, nous présentons une approche "hyper-jeu'' comme outil d'analyse entre agents dans le cadre d'un environnement multi-agent. Nous donnons un aperçu (très succinct) de la formalisation d'un tel hyper-jeux et nous expliquerons ensuite, comment les agents pourraient intervenir via un agent-médiateur quand ils ont des perceptions différentes. Après cela, nous expliquerons comment les agents pourraient tirer avantage des perceptions différentes.Game Theory, Hypergame, Mediation, Théorie des jeux, hyper-jeux, médiation

Classifying efficiently the behavior of a Soccer team

Proceeding of: 10th International Conference on Intelligent Autonomous Systems (IAS 2008), Baden Baden, Germany, July 23-25th, 2008.In order to make a good decision, humans usually try to predict the behavior of others. By this prediction, many different tasks can be performed, such as to coordinate with them, to assist them or to predict their future behavior. In competitive domains, to recognize the behavior of the opponent can be very advantageous. In this paper, an approach for creating automatically the model of the behavior of a soccer team is presented. This approach is an effective and notable improvement of a previous work. As the actions performed by a soccer team are sequential, this sequentiality should be considered in the modeling process. Therefore, the observations of a soccer team in a dynamic, complex and continuous multi-variate world state are transformed into a sequence of atomic behaviors. Then, this sequence is analyzed in order to find out a model that defines the team behavior. Finally, the classification of an observed team is done by using a statistical test.This work has been supported by the Spanish Ministry of Education and Science under project TRA-2007-67374-C02-02.Publicad

Agent Modeling as Auxiliary Task for Deep Reinforcement Learning

In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling. Inspired by recent works on representation learning and multiagent deep reinforcement learning, we propose two architectures to perform agent modeling: the first one based on parameter sharing, and the second one based on agent policy features. Both architectures aim to learn other agents' policies as auxiliary tasks, besides the standard actor (policy) and critic (values). We performed experiments in both cooperative and competitive domains. The former is a problem of coordinated multiagent object transportation and the latter is a two-player mini version of the Pommerman game. Our results show that the proposed architectures stabilize learning and outperform the standard A3C architecture when learning a best response in terms of expected rewards.Comment: AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE'19

Latent Emission-Augmented Perspective-Taking (LEAPT) for Human-Robot Interaction

Perspective-taking is the ability to perceive or understand a situation or concept from another individual's point of view, and is crucial in daily human interactions. Enabling robots to perform perspective-taking remains an unsolved problem; existing approaches that use deterministic or handcrafted methods are unable to accurately account for uncertainty in partially-observable settings. This work proposes to address this limitation via a deep world model that enables a robot to perform both perception and conceptual perspective taking, i.e., the robot is able to infer what a human sees and believes. The key innovation is a decomposed multi-modal latent state space model able to generate and augment fictitious observations/emissions. Optimizing the ELBO that arises from this probabilistic graphical model enables the learning of uncertainty in latent space, which facilitates uncertainty estimation from high-dimensional observations. We tasked our model to predict human observations and beliefs on three partially-observable HRI tasks. Experiments show that our method significantly outperforms existing baselines and is able to infer visual observations available to other agent and their internal beliefs

Learning Models of Adversarial Agent Behavior under Partial Observability

The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction. In this work, we present Graph based Adversarial Modeling with Mutal Information (GrAMMI) for modeling the behavior of an adversarial opponent agent. GrAMMI is a novel graph neural network (GNN) based approach that uses mutual information maximization as an auxiliary objective to predict the current and future states of an adversarial opponent with partial observability. To evaluate GrAMMI, we design two large-scale, pursuit-evasion domains inspired by real-world scenarios, where a team of heterogeneous agents is tasked with tracking and interdicting a single adversarial agent, and the adversarial agent must evade detection while achieving its own objectives. With the mutual information formulation, GrAMMI outperforms all baselines in both domains and achieves 31.68% higher log-likelihood on average for future adversarial state predictions across both domains.Comment: 8 pages, 3 figures, 2 table

Programming Robosoccer agents by modelling human behavior

The Robosoccer simulator is a challenging environment for artificial intelligence, where a human has to program a team of agents and introduce it into a soccer virtual environment. Most usually, Robosoccer agents are programmed by hand. In some cases, agents make use of Machine learning (ML) to adapt and predict the behavior of the opposite team, but the bulk of the agent has been preprogrammed. The main aim of this paper is to transform Robosoccer into an interactive game and let a human control a Robosoccer agent. Then ML techniques can be used to model his/her behavior from training instances generated during the play. This model will be used later to control a Robosoccer agent, thus imitating the human behavior. We have focused our research on low-level behavior, like looking for the ball, conducting the ball towards the goal, or scoring in the presence of opponent players. Results have shown that indeed, Robosoccer agents can be controlled by programs that model human play.Publicad

CAOS Coach 2006 Simulation Team: An Opponent Modelling Approach

Agent technology represents a very interesting new means for analyzing, designing and building complex software systems. Nowadays, agent modelling in multi-agent systems is increasingly becoming more complex and significant. RoboCup Coach Competition is an exciting competition in the RoboCup Soccer League and its main goal is to encourage research in multii-agent modelling. This paper describes a novel method used by the team CAOS (CAOS Coach 2006 Simulation Team) in this competition. The objective of the team is to model successfully the behaviour of a multi-agent system