Dimitrios Antos

AAMAS Conference 2012 Conference Paper

Bayesian Model of the Social Effects of Emotion in Decision-Making in Multiagent Systems

• Celso de Melo
• Peter Carnevale
• Stephen Read
• Dimitrios Antos
• Jonathan Gratch

Research in the behavioral sciences suggests that emotion can serve important social functions and that, more than a simple manifestation of internal experience, emotion displays communicate one's beliefs, desires and intentions. In a recent study we have shown that, when engaged in the iterated prisoner's dilemma with agents that display emotion, people infer, from the emotion displays, how the agent is appraising the ongoing interaction (e. g. , is the situation favorable to the agent? Does it blame me for the current state-of-affairs? ). From these appraisals people, then, infer whether the agent is likely to cooperate in the future. In this paper we propose a Bayesian model that captures this social function of emotion. The model supports probabilistic predictions, from emotion displays, about how the counterpart is appraising the interaction which, in turn, lead to predictions about the counterpart's intentions. The model's parameters were learned using data from the empirical study. Our evaluation indicated that considering emotion displays improved the model's ability to predict the counterpart's intentions, in particular, how likely it was to cooperate in a social dilemma. Using data from another empirical study where people made inferences about the counterpart's likelihood of cooperation in the absence of emotion displays, we also showed that the model could, from information about appraisals alone, make appropriate inferences about the counterpart's intentions. Overall, the paper suggests that appraisals are valuable for computational models of emotion interpretation. The relevance of these results for the design of multiagent systems where agents, human or not, can convey or recognize emotion is discussed.

AAMAS Conference 2011 Conference Paper

Human Factors in Computer Decision-Making (PhD Thesis Extended Abstract)

• Dimitrios Antos

This thesis investigates whether incorporating ideas from human decision-making in computer algorithms may help improve agents' decision-making performance, as either independent actors or in collaboration with humans. For independent actors, psychological cognitive appraisal theories of emotion are used to develop a lightweight algorithm that dynamically re-prioritizes their goals to direct their attention. In experiments in quickly changing and highly uncertain domains these agents are shown to perform as well as agents that compute expensive optimal solutions, and exhibit robustness with respect to the parameters of the environment. For agents interacting with humans, it is investigated whether expressing emotions has the ability to convey traits like trustworthiness and skill, and whether the appropriate emotional expression can help forge mutually beneficial relationships with the human. Finally, the theory of reasoning patterns is leveraged to analyze games and make it possible to answer questions about a system's strategic behavior without having to compute an expensive, precise solution. This theory is also employed to the generate advice for human decision-makers in complex games. This advice has been experimentally shown to improve their decision-making performance.

AAMAS Conference 2011 Conference Paper

Reasoning Patterns in Bayesian Games

• Dimitrios Antos
• Avi Pfeffer

Bayesian games have been traditionally employed to describe and analyze situations in which players have private information or are uncertain about the game being played. However, computing Bayes-Nash equilibria can be costly, and becomes even more so if the common prior assumption (CPA) has to be abandoned, which is sometimes necessary for a faithful representation of real-world systems. We propose using the theory of reasoning patterns in Bayesian games to circumvent some of these difficulties. The theory has been used successfully in common knowledge (non-Bayesian) games, both to reduce the computational cost of finding an equilibrium and to aid human decision-makers in complex decisions. In this paper, we first show that reasoning patterns exist for every decision of every Bayesian game, in which the acting agent has a reason to deliberate. This implies that reasoning patterns are a complete characterization of the types of reasons an agent might have for making a decision. Second, we illustrate practical applications of reasoning patterns in Bayesian games, which allow us to answer questions that would otherwise not be easy in traditional analyses, or would be extremely costly. We thus show that the reasoning patterns can be a useful framework in analyzing complex social interactions.

AAAI Conference 2011 Conference Paper

The Influence of Emotion Expression on Perceptions of Trustworthiness in Negotiation

• Dimitrios Antos
• Celso de Melo
• Jonathan Gratch
• Barbara Grosz

When interacting with computer agents, people make inferences about various characteristics of these agents, such as their reliability and trustworthiness. These perceptions are significant, as they influence people’s behavior towards the agents, and may foster or inhibit repeated interactions between them. In this paper we investigate whether computer agents can use the expression of emotion to influence human perceptions of trustworthiness. In particular, we study human-computer interactions within the context of a negotiation game, in which players make alternating offers to decide on how to divide a set of resources. A series of negotiation games between a human and several agents is then followed by a “trust game. ” In this game people have to choose one among several agents to interact with, as well as how much of their resources they will trust to it. Our results indicate that, among those agents that displayed emotion, those whose expression was in accord with their actions (strategy) during the negotiation game were generally preferred as partners in the trust game over those whose emotion expressions and actions did not mesh. Moreover, we observed that when emotion does not carry useful new information, it fails to strongly influence human decision-making behavior in a negotiation setting.

IJCAI Conference 2011 Conference Paper

Using Emotions to Enhance Decision-Making

• Dimitrios Antos
• Avi Pfeffer

We present a novel methodology for decision-making by computer agents that leverages a computational concept of emotions. It is believed that emotions help living organisms perform well in complex environments. Can we use them to improve the decision-making performance of computer agents? We explore this possibility by formulating emotions as mathematical operators that serve to update the relative priorities of the agent's goals. The agent uses rudimentary domain knowledge to monitor the expectation that its goals are going to be accomplished in the future, and reacts to changes in this expectation by "experiencing emotions. " The end result is a projection of the agent's long-run utility function, which might be too complex to optimize or even represent, to a time-varying valuation function that is being myopically maximized by selecting appropriate actions. Our methodology provides a systematic way to incorporate emotion into a decision-theoretic framework, and also provides a principled, domain-independent methodology for generating heuristics in novel situations. We test our agents in simulation in two domains: restless bandits and a simple foraging environment. Our results indicate that emotion-based agents outperform other reasonable heuristics for such difficult domains, and closely approach computationally expensive near-optimal solutions, whenever these are computable, yet requiring only a fraction of the cost.

AAMAS Conference 2010 Conference Paper

Representing Bayesian Games Without a Common Prior

• Dimitrios Antos
• Avi Pfeffer

Game-theoretic analyses of multi-agent systems typically assume that all agents have full knowledge of everyone's possible moves, information sets and utilities for each outcome. Bayesian games relax this assumption by allowing agents tohave different "types, " representing different beliefs aboutthe game being played, and to have uncertainty over otheragents' types. However, applications of Bayesian games almost universally assume that all agents share a commonprior distribution over everyone's type. We argue, in concordwith certain economists, that such games fail to accuratelyrepresent many situations. However, when the commonprior assumption is abandoned, several modeling challengesarise, one of which is the emergence of complex belief hierarchies. In these cases it is necessary to specify which partsof other agents' beliefs are relevant to an agent's decision-making (or need be known by that agent). We address thisissue by suggesting a concise way of representing Bayesiangames with uncommon priors. Our representation centersaround the concept of a block, which groups agents' view of(a) the game being played and (b) their posterior beliefs. This allows us to construct the belief graph, a graphicalstructure that allows agents' knowledge of other agents' beliefs to be carefully specified. Furthermore, when agents'views of the world are represented by extensive form games, our block structure places useful semantic constraints onthe extensive form trees. Our representation can be usedto naturally represent games with rich belief structures andinteresting predicted behavior.

IJCAI Conference 2009 Conference Paper

• Dimitrios Antos
• Avi Pfeffer

We propose a novel method for helping humans make good decisions in complex games, for which common equilibrium solutions may be too difficult to compute or not relevant. Our method leverages and augments humans’ natural use of arguments in the decision making process. We believe that, if computers were capable of generating similar arguments from the mathematical description of a game, and presented those to a human decision maker, the synergies would result in better performance overall. The theory of reasoning patterns naturally lends itself to such a use. We use reasoning patterns to derive localized evaluation functions for each decision in a game, then present their output to humans. We have implemented this approach in a repeated principal-agent game, and used it to generate advice given to subjects. Experimental results show that humans who received advice performed better than those who did not.

UAI Conference 2008 Conference Paper

Identifying reasoning patterns in games

• Dimitrios Antos
• Avi Pfeffer

We present an algorithm that identifies the reasoning patterns of agents in a game, by iteratively examining the graph structure of its Multi-Agent Influence Diagram (MAID) representation. If the decision of an agent participates in no reasoning patterns, then we can effectively ignore that decision for the purpose of calculating a Nash equilibrium for the game. In some cases, this can lead to exponential time savings in the process of equilibrium calculation. Moreover, our algorithm can be used to enumerate the reasoning patterns in a game, which can be useful for constructing more effective computerized agents interacting with humans.

AAAI Conference 2008 Short Paper

Using Reasoning Patterns to Simplify Games

• Dimitrios Antos

In complex strategic situations decision-making agents interact with many other agents and have access to many pieces of information throughout their play. This usually leads to game solving being a very complex, almost intractable procedure. Moreover, algorithms for solving games usually fail to explain how the various equilibria come about and how “plausible” they are. Reasoning patterns try to capture the strategic thinking of agents and formalize the usage of the various information or evidence they obtain during their interactions. Identifying reasoning patterns can lead to a significant refinement over the full range of equilibria, as well as considerable computational savings in solving the game. Here we present a polynomial-time algorithm that simplifies the original game by iteratively identifying noneffective (ignorable) decision nodes and removing redundant information edges. In some cases, this can lead to exponential-time savings in computing an equilibrium, yet some –potentially efficient– equilibria may be lost in the process.