Engineering distributed protocols for multi-agent interactions using game theory

This collection began as a conference on Modeling Rational and Moral Agents that combined two themes. First is the problematic place of morality within the received theory of rational choice. Decision theory, game theory, and economics are unfriendly to crucial features of morality, such as commitment to promises. But since morally constrained agents seem to do better than rational agents - say by co-operating in social situations like the Prisoner’s Dilemma - it is difficult to dismiss them as simply irrational. The second theme is the use of modeling techniques. We model rational and moral agents because problems of decision and interaction are so complex that there is much to be learned even from idealized models. The two themes come together in the most obvious feature of the papers: the common use of games, like the Prisoner’s Dilemma (PD), to model social interactions that are problematic for morality and rationality.’

The article proposes a new approach to reasoning about knowledge and strategies in multiagent systems. It emphasizes data, not agents, as the source of strategic knowledge. The approach brings together Armstrong's functional dependency expression from database theory, a data-informed knowledge modality based on a recent work by Baltag and van Benthem, and a newly proposed data-informed strategy modality. The main technical result is a sound and complete logical system that describes the interplay between these three logical operators.

Multi-agent systems defined on a network can be used for modelling the competition between companies in terms of market dominance. In view of the enormous size of the search space for winning strategies of initial configuration of resource allocation on network, we focus our search on the subspace defined by special local clustering effects, using the recently developed evolutionary computational algorithm. Strategies that emphasize local solidarity, measured by the formation of clusters in the form of triangles linkage between members of the same company, prove to be effective in winning both the market share with high probability and high speed. The result provides a good guideline to improve the collective competitiveness in a network of agents. The formulation is based on the Ising model in statistical physics and the evolutionary game is based on Monte Carlo simulation. Significance and the application of the algorithm in the context of econophysics and damage spreading in network are discussed.

Experimental analysis of agent strategies in multiagent systems presents a tradeoff between granularity and statistical confidence. Collecting a large amount of data about each strategy profile improves confidence, but restricts the range of strategies and profiles that can be explored. We propose a flexible approach, where multiple game-theoretic formulations can be constructed to model the same underlying scenario (observation dataset). The prospect of incorrectly selecting an empirical model is termed generalization risk, and the generalization risk framework we describe provides a general criterion for empirical modeling choices, such as adoption of factored strategies or other structured representations of a game model. We propose a principled method of managing generalization risk to derive the optimal game-theoretic model for the observed data in a restricted class of models. Application to a large dataset generated from a trading agent scenario validates the method.

Electric vehicles and hybrid vehicles have become one of the relevant and sustainable solutions for the transportation of tomorrow. They are the most presenting and studying by the research; seen their advantages in terms of composition and movement. Within this type of vehicles, energy management is a fundamental tool to ensure smooth operation during the rolling cycle, while optimizing available energy sources and even predicting future power demand. This paper proposes a method of optimization and prediction of energy based on the Markov chains. The work requires the development of a suitable system to control the electric model of powertrain: the Electric motor and the Hybrid Electric Source (HES). The given studied vehicle has a HES to processing in real-time. It is, therefore, interesting to define a flexible and fast management system, which can process the available sub-components. We use, therefore, a multi-agent system (MAS) to intelligently manage the available energy in a Fuel Cell (FC) and Super-capacitor (SC). During the simulation stage, we seek to apply an online prediction and see how the system reacts in terms of optimized power. Comparing also the results found by the adopted method to others of the same case studied. This allowing well define the most efficient and suitable laws of management for an HEV.

Uncomputability and undecidability in economic theory

Protecting critical infrastructure is a challenging task for security agencies all around the world, which is exacerbated by terrorism. Many infrastructures have network characteristics, such as transportation and communication systems. Therefore, it is necessary to study them from the perspective of network science. Game theory provides a suitable theoretical tool for the study of attack-defense confrontation between rational agents. Sequential game, which is more in line with the actual situation, can better analyze the confrontation in infrastructure protection. Previous models which combine network science with game theory only consider the attacker as a single type. However, in the real world, accurate information about the type of the attacker is not always available for the defender. In this paper, we apply Bayesian Stackelberg game to model the attack-defense confrontation in the case of uncertain attacker type.

This paper presents a new event-B based approach to reasoning about interaction protocols. We show how an event-B model can be structured from AUML protocol diagrams and then used to give a formal semantic to protocol diagrams which supports proofs of their correctness. More precisely, we give rules for the translation of protocol diagrams into event-B language. In particular, we focus on the translation of messages with response delay. The event-B language allows the definition of invariant describing required interaction properties and provides an automatic proof. By an example of multi-agent systems interaction protocol, we illustrate the proposed translation.

In the recent decade, people have attached increasingly greater importance to healthy diet demands; facilitating rapid growth of organic agriculture. However, methods for organic cultivation have great differences from traditional methods. How to effectively choose appropriate organic crops, for season changes and different farming conditions, is a difficult issue for the majority of planters. In addition, achieving the goal of maximum economic profits for the year must target all crops, and should be evaluated as the basis of priority selection. Taking organic vegetable farming as an example, this research uses knowledge and rule-based methods, while applying a game and multi-agent theory, and develops a set of graphic intellectual decision evaluation mechanisms with ASP.NET and MS-SQL. In the system, a merger of game theory and multi-agent system was tested and verified to provide suggestions that are 84.25% effective, as compared to the suggestions provided by experts. Future studies will apply the theory on other crop planting issues.

Game theoretic decision making is a practical approach to multi-agent coordination. Rational agents may make decisions based on different principles of rationality assumptions that usually involve knowledge of how other agents might move. After formulating a game matrix of utility entries of possible combination of moves from both agents, agents can reason which combination is the equilibrium. Most previous game theoretic works treat the utility values qualitatively (i.e., consider only the order of the utility values). This is not practical since the utility values are usually approximate and the differences between utility values are somewhat vague. In this paper, we present a fuzzy game theoretic decision making mechanism that can deal with uncertain utilities. We thus construct a fuzzy-theoretic game framework under both the fuzzy theory and the game theory. The notions of fuzzy dominant relations, fuzzy Nash equilibrium, and fuzzy strategies are defined and fuzzy reasoning are carried out in agent decision making. We show that a fuzzy strategy can perform better than a mixed strategy in traditional game theory in dealing with more than one Nash equilibrium games.KeywordsNash EquilibriumFuzzy NumberMixed StrategyDominant StrategyStrategy CombinationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The aim of our research is to understand and automate the mechanisms by which language can emerge among artificial, knowledge-based and rational agents. We want to design and implement agents that, upon encountering other agent(s) with which they do not share an agent communication language, are able to initiate creation of, and further are able to evolve and enrich, a mutually understandable communication language. Our research is supported by the principled methodology of designing rational, socially competent artificial agents based on Bayesian probability and decision theories, and on the research in linguistics and cognitive psychology that addresses the issues of function, mechanisms, development, and evolutionary history of natural languages. In our work, we express some of the key insights obtained in linguistics and cognitive science in formal terms of decision theory and game-theoretic. mechanism design. We propose that the evolution of an agent communication language can be accomplished by the mechanism of negotiation, developed in economics and game theory, and automated in recent work in artificial intelligence. Negotiation is suitable because it can be mapped to settings in which rational interacting agents could use communication for their mutual, yet selfish, benefits. The agents can make mutually beneficial agreements that will allow efficient communication, but they have a conflict of interest about which language constructs to use each would prefer a communication language that is easier and less costly to use from their own individual perspective.

The existing Workflow Management Systems, WfMS (ACTIONTm, Xsoft InConcert, Team Ware) show some insufficient due essentially to two factors. First, the enterprises express a real need of WfMS that deal with flexible business processes. These later must adapt to the optimization of enterprises work methods and the evolution of their needs. Second, the enterprises also need of control mechanisms and modeling tools for their WfMS in order to design robust workflow models assuring an efficient and flexible processing of errors and exceptions of execution. In others words, the WfMS must deal with Workflow mining to support Business Processes Re-engineering (BPR). A BPR is a method to improve the effectiveness and efficiency of business processes. Existing propositions in the literature are rather dedicated to the process perspective mining and they neglect the discovering of organizational perspective although it is decisive for the enhancement of existing workflow or the proposing of a new Workflow. The paper addresses the workflow mining by considering the organizational perspective. By organizational perspective, we mean the organizational structures (Federation, Hierarchical, Market and so on) and interaction protocols (Contract net, Auction...) describing respectively the forms of work and the activities allocation process between actors. This paper defends the idea that organizational dimension in multi-agent system is an appropriate approach to discover this organizational perspective since it (i) provides inter-agents communication languages based on performatives which allow capturing the semantic of exchanges between actors, (ii) it introduces organizational structures to highlight the forms of work and (iii) it offers powerful interactions protocols which permit to identify the dynamic organizational structures. First, this paper proposes a Workflow log meta-model which extends the classical one. Then, it provides some algorithms to permit the discovering of the main organizational structures and interaction protocols. Third, it presents the Management process of Water distribution crisis case study to illustrate our approach. Finally, the paper describes the developed prototype, called DiscopFlow, in order to validate the proposed solution.

Multi-agent systems are attractive means for developing complex software systems. However, multi-agent systems themselves tend to be complex, and certain difficulties exist in developing them. One of the difficulties is the gap between design and implementation especially for interaction protocols. In this paper, we propose a new interaction protocol description language called IOM/T. Interaction protocols described using IOM/T have clear correspondence with AUML sequence diagrams and the description can be consolidated into a single unit of IOM/T code. Then, we show how the process of implementing Java-based agent-platform code from AUML design can be carried out, and how IOM/T effectively bridges the gap between design and implementation.

Most agent architectures implement autonomous agents that use extensive interaction protocols and social laws to control interactions in order to ensure that the correct behaviors result during run-time. These agents, organized into multi-agent systems in which all agents adhere to predefined interaction protocols, are well suited to the analysis, design and implementation of complex systems in environments where it is possible to predict interactions during the analysis and design phases. In these multi-agent systems, intelligence resides in individual autonomous agents, rather than in the collective behavior of the individual agents. These agents are commonly referred to as “next-generation” or intelligent components, which are difficult to implement using current component-based architectures. In most distributed environments, such as the Internet, it is not possible to predict interactions during analysis and design. For a complex system to be able to adapt in such an uncertain and non-deterministic environment, we propose the use of agencies, consisting of simple agents, which use probabilistic reasoning to adapt to their environment. Our agents collectively implement distributed Bayesian networks, used by the agencies to control behaviors in response to environmental states. Each agency is responsible for one or more behaviors, and the agencies are structured into heterarchies according to the topology of the underlying Bayesian networks. We refer to our agents and agencies as “Bayesian agents” and “Bayesian agencies.”

This paper presents the support for interaction protocols that has been recently added to the JADEL programming language. JADEL is an agent-oriented programming language designed to ease the development of JADE agents and multi-agent systems by offering general-purpose agent-oriented abstractions to the programmer. The first part of the paper presents JADEL and motivates the need for a new agent-oriented programming language. Then, the agent-oriented abstractions that JADEL has been providing since its first version-namely agents, behaviours and communication ontologies-are described. The new support for interaction protocols is finally presented, and a simple example of a JADEL multi-agent system that uses interaction protocols is reported. The paper is concluded with an overview of the current state of JADEL and related tools.