Agent Interaction Model Research Articles

Stochastic pairwise preference convergence in Bayesian agents.

Beliefs inform the behavior of forward-thinking agents in complex environments. Recently, sequential Bayesian inference has emerged as a mechanism to study belief formation among agents adapting to dynamical conditions. However, we lack critical theory to explain how preferences evolve in cases of simple agent interactions. In this paper, we derive a Gaussian, pairwise agent interaction model to study how preferences converge when driven by observation of each other's behaviors. We show that the dynamics of convergence resemble an Ornstein-Uhlenbeck process, a common model in nonequilibrium stochastic dynamics. Using standard analytical and computational techniques, we find that the hyperprior magnitudes, representing the learning time, determine the convergence value and the asymptotic entropy of the preferences across pairs of agents. We also show that the dynamical variance in preferences is characterized by a relaxation time t^{★} and compute its asymptotic upper bound. This formulation enhances the existing toolkit for modeling stochastic, interactive agents by formalizing leading theories in learning theory, and builds towards more comprehensive models of open problems in principal-agent and market theory.

Soft actor-critic DRL algorithm for interval optimal dispatch of integrated energy systems with uncertainty in demand response and renewable energy

The collaborative optimization dispatching of multiple energy flows plays a crucial role in achieving the economic and efficient low-carbon operation of integrated energy systems (IESs). However, the dispatching problem for IESs is characterized by high dimensionality, non-linearity, and complex coupling. Furthermore, the integration of renewable energy sources and flexible loads has transformed the IES into a complex dynamic system with significant uncertainty. Traditional intelligent optimization algorithms exhibit poor adaptability and lengthy solution computation time when tackling such problems. In contrast, deep reinforcement learning (DRL), as an interactive trial-and-error learning method, has shown improved decision-making capabilities. In view of this, a data-driven soft actor-critic (SAC) deep reinforcement learning-based approach is proposed in this paper for interval optimal dispatch of IESs considering multiple uncertainties. First, the basic principle of SAC reinforcement learning is introduced in detail, and the basic framework of reinforcement learning for interval optimal scheduling of IESs is constructed. Then, the environment model of agent interaction is constructed, and the action and state space of DRL, as well as the reward mechanism and neural network structure, are designed. Finally, a typical IES case is experimentally analyzed and compared with three popular DRL algorithms and five state-of-the-art intelligent optimization algorithms. The experimental results demonstrate the advantages and effectiveness of the proposed method in solving the optimal dispatching of IESs.

Global air transport complex network: multi-scale analysis

Almost half of the world’s population is carried by airlines each year, and understanding this mode of transport is important from economic and scientific perspectives. In recent years, the increasing availability of data has led to complex network and agent interaction models which attempt to gain better understanding of the air transport network and develop forecasts. In this case study paper, we review existing research on two key approaches, namely: (1) a top-down multi-scale network science approach, and (2) a bottom-up entropy-maximization interaction network approach. Using simple socioeconomic indicators, we were able to construct a very accurate interaction model that can predict traffic volume, and the model can forward estimate the impact of population growth or fuel cost. Using network science approaches, we were able to identify community structures and relate them to economic outputs. We also saw how hubs evolved over time to become more influential. Looking into the future, using random graph theory, it seems that reduced flight cost will lead to increased hub influence. The disseminated knowledge in this case study paper will provide both academics and industry practitioners with steps forward to co-explore the interesting research landscape.

Secure information sharing in social agent interactions using information flow analysis

When we wish to coordinate complex, cooperative tasks in open multi-agent systems, where each agent has autonomy and the agents have not been designed to work together, we need a way for the agents themselves to determine the social norms that govern collective behaviour. An effective way to define social norms for agent communication is through the use of interaction models such as those expressed in the Lightweight Coordination Calculus (LCC), a compact executable specification language based on logic programming and pi-calculus. Open multi-agent systems have experienced growing popularity in the multi-agent community and gain importance as large scale distributed systems become more widespread. A major practical limitation to such systems is security, because the very openness of such systems opens the doors to adversaries to exploit vulnerabilities introduced through acceptance of social norms.This paper addresses a key vulnerability of security of open multi-agent systems governed by formal models of social norms (as exemplified by LCC). A fundamental limitation of conventional security mechanisms (e.g. access control and encryption) is the inability to prevent information from being propagated. Focusing on information leakage in choreography systems using LCC, we suggest a framework to detect insecure information flows. A novel security-typed LCC language is proposed to prevent information leakage.Both static (design-time) and dynamic (run-time) security type checking are employed to guarantee no information leakage can occur in annotated agent interaction models. The proposed security type system is discussed and then formally evaluated by proving its properties. Two disadvantages of the pure dynamic analysis are its late detection and its inability to detect implicit information flows. We overcome these issues by performing static analysis. The proposed security type system supports non-interference, i.e. high-security input to the program never affect low-security output. However, it disregards information leaks due to the termination of the program.

The design of software-agent architecture for customer relationship management in utility service-based business

A challenge facing businesses establishing an effective customer relationship management (CRM) system is the need for integrating data from different sources. Many data integration approaches have been proposed and widely adopted to improve customer services. However, not all data integration approaches are suitable for utility service-based business due to its unique characteristics. The study explores the idea of using software agents to address the integration issue of building CRM systems for utility service-based businesses. The study proposes two simple agent interaction models, the request-response model and the subscription-publishing model as well as six categories of agents to meet the need. The utility and validity of the proposed approach are examined and verified in a real-world case. They are implemented with extensible markup language (XML) and applied to create an agent-based architecture for a CRM system for a telecommunication company in Taiwan.

Learning agent influence in MAS with complex social networks

In complex open multi-agent systems (MAS), where there is no centralised control and individuals have equal authority, ensuring cooperative and coordinated behaviour is challenging. Norms and conventions are useful means of supporting cooperation in an emergent decentralised manner, however it takes time for effective norms and conventions to emerge. Identifying influential individuals enables the targeted seeding of desirable norms and conventions, which can reduce the establishment time and increase efficacy. Existing research is limited with respect to considering (i) how to identify influential agents, (ii) the extent to which network location imbues influence on an agent, and (iii) the extent to which different network structures affect influence. In this paper, we propose a methodology for learning a model for predicting the network value of an agent, in terms of the extent to which it can influence the rest of the population. Applying our methodology, we show that exploiting knowledge of the network structure can significantly increase the ability of individuals to influence which convention emerges. We evaluate our methodology in the context of two agent-interaction models, namely, the language coordination domain used by Salazar et al. (AI Communications 23(4): 357–372, 2010) and a coordination game of the form used by Sen and Airiau (in: Proceedings of the 20th International Joint Conference on Artificial Intelligence, 2007) with heterogeneous agent learning mechanisms, and on a variety of synthetic and real-world networks. We further show that (i) the models resulting from our methodology are effective in predicting influential network locations, (ii) there are very few locations that can be classified as influential in typical networks, (iii) four single metrics are robustly indicative of influence across a range of network structures, and (iv) our methodology learns which single metric or combined measure is the best predictor of influence in a given network.

Stochastic model of agent interaction with opinion leaders

We analyze the problem of agents' interactions in a given population. The purpose of this paper is twofold. Starting from a scheme proposed by Galam [Physica A 320, 571 (2003)], which is based on a majority rule to treat the individuals' interactions, we first study some of its relevant properties. Then, we introduce special individuals, called opinion leaders, who play a key role in information spreading in several practical applications. Opinion leaders have the special feature of strongly interfering with the process based on the majority rule, speeding up the diffusion. We consider a model describing agents' interactions, which encompasses Galam's proposal, where opinion leaders are included as special agents. Then we study its specific properties which significantly recast and extend some conclusions drawn for the models given by Galam and Ellero, Fasano, and Sorato [Physica A 388, 3901 (2009)]. Finally, we provide theoretical and numerical results concerning the dynamics of our model, showing that a small percentage of opinion leaders may both accelerate and/or even reverse the overall consensus among all the agents.

Ping Pong in Dangerous Graphs: Optimal Black Hole Search with Pebbles

We prove that, for the black hole search problem in networks of arbitrary but known topology, the pebble model of agent interaction is computationally as powerful as the whiteboard model; furthermore the complexity is exactly the same. More precisely, we prove that a team of two asynchronous agents, each endowed with a single identical pebble (that can be placed only on nodes, and with no more than one pebble per node), can locate the black hole in an arbitrary network of known topology; this can be done with Θ(nlog n) moves, where n is the number of nodes, even when the links are not FIFO. These results are obtained with a novel algorithmic technique, ping-pong, for agents using pebbles.

Modeling of intelligent agent interactions for multiagent systems

The experience gained in the development of the system of simulation modeling of intelligent agent interaction (SMIAI) is described. It is based on the general model of agent interaction proposed by G.V. Rybina, who developed the works on intelligent dialog systems, including the well-known Popov's communication model, in the late 1990s. The goal of SMIAI is to validate the applicability of the proposed models, methods, and algorithms of intelligent agent interactions and their architecture in developing MAS prototypes for different problem areas. The SMIAI architecture, support means for simulation experiments, transmission and processing of messages, and other components are considered. Realized MAS prototypes for several applications are presented as examples. The models of local, thematic, and global dialog structure for intelligent agents are presented and the problems of intelligent agent communications behavior formation are analyzed. The creation of the models under consideration by means of G2 and Microsoft Visual Studio is described.

Role-based software agent interaction models: a survey

AbstractThe role concept represents a useful approach that has been exploited in different agent-based systems, in particular applied to interactions between agents. There are some requirements that are important for the development of agent-based applications using roles, for instance the support for the analysis, the design and the implementation phases. We have considered and compared different role-based proposals in the literature, and this paper presents a survey of the most spread ones. We explain each proposal and point out if and how it meets the identified requirements. Far from deciding the best proposal, our aim is to present the advantages and drawbacks of several proposals to designers and developers, so that they can make the best choice with regard to their needs.

A unification-based approach to configure generic protocols into agent interaction models

This paper presents a new approach for generic protocol configuration in agents' internal models. Generic protocols are reusable predefined sequences of message exchanges. Their specifications are most of the time merged with the internal architecture of agents. This requires designers to introduce them by hand every time it is needed. It results in inconsistencies that may occur in message exchange at run time. Inconsistent message exchange in turn results in the failure of the associated interaction. First, we introduce a new language to specify generic protocols. Then we generate the interaction model of agents using their functional model, a generalisation of the design model (only the part devoted to interaction). In order to generate the interaction model, we developed a unification mechanism to automatically check the compliance of a protocol specification with the elements of a functional model. We argue that our approach guarantees that the implementation of the interaction model complies with the initial specification of the protocols it is based on. Here, we discuss the knowledge and reasoning mechanism involved in our approach. An implementation of our approach is also discussed. Particularly, we show the approach's effectiveness with real-world and complex applications.

Автоматизированный синтез агентов при создании мультиагентных систем

The agent synthesis method for the distributed intelligent system using the ontological knowledge models has been developed. The agent interaction model for the multi-agent system using Semantic Web standards and technologies has been suggested. The agent knowledge model has been developed and formally described using description logics.

The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma

Many economic and social systems are essentially large multi-agent systems. By means of computational modeling, the complicated behavior of such systems can be investigated. Modeling a multi-agent system as an evolutionary agent system, several important choices have to be made for evolutionary operators. Especially, it is to be expected that evolutionary dynamics substantially depend on the selection scheme. We therefore investigate the influence of evolutionary selection mechanisms on a fundamental problem: the iterated prisoner's dilemma (IPD), an elegant model for the emergence of cooperation in a multi-agent system. We observe various types of behavior, cooperation level, and stability, depending on the selection mechanism and the selection intensity. Hence, our results are important for (1) the proper choice and application of selection schemes when modeling real economic situations and (2) assessing the validity of the conclusions drawn from computer experiments with these models. We also conclude that the role of selection in the evolution of multi-agent systems should be investigated further, for instance using more detailed and complex agent interaction models.