A normative framework for agent-based systems

One of the key issues in the computational representation of open societies relates to the introduction of norms that help to cope with the heterogeneity, the autonomy and the diversity of interests among their members. Research regarding this issue presents two omissions. One is the lack of a canonical model of norms that facilitates their implementation, and that allows us to describe the processes of reasoning about norms. The other refers to considering, in the model of normative multi-agent systems, the perspective of individual agents and what they might need to effectively reason about the society in which they participate. Both are the concerns of this paper, and the main objective is to present a formal normative framework for agent-based systems that facilitates their implementation. F. López y López is researcher of the Computer Science Faculty at the Benemérita Universidad Autónoma de Puebla in México, from where she got her first degree. She also gained a MSc in Computation from the Universidad Nacional Autónoma de México and a PhD in Computer Science from the University of Southampton in the United Kingdom. She is leading several theoretical and practical projects that use multi-agent systems as the main paradigm. Her research has been focused on Autonomous Normative Agents and Normative Multi-Agent Systems and she has published over 20 articles in these and related topics. M. Luck is Professor of Computer Science in the Intelligence, Agents, Multimedia Group of the School of Electronics and Computer Science at the University of Southampton, where he carries out research into the theory and practice of agent technology. He has published over 150 articles in these and related areas, both alone and in collaboration with others, and has published eight books. He is a member of the Executive Committee of AgentLink III, the European Network of Excellence for Agent-Based Computing. He is a co-founder of the European Multi-Agent Systems workshop series, is co-founder and Chair of the steering committee of the UK Multi-Agent Systems Workshops (UKMAS), and was a member of the Management Board of Agentcities.NET. Professor Luck is also a steering committee member for the Central and Eastern European Conference on Multi-Agent Systems. He is series editor for Artech House’s Agent Oriented Systems series, and an editorial board member of the Journal of Autonomous Agents and Multi-Agent Systems, the International Journal of Agent-Oriented Software Engineering, and ACM Transactions on Autonomous and Adaptive Systems. M. d’Inverno gained a BA in Mathematics and an MSc in Computation both from Oxford University. He also was awarded a PhD from University College London. He joined the University of Westminster in 1992 as a Lecturer, became a senior lecturer in 1998, a reader in 1999 and was appointed professor of computer science in 2001. He is interested in formal, principled approaches to modelling both natural and artificial systems in a computational setting. The main strand to this research, focuses on the application of formal methods in providing models of intelligent agent and multi-agent systems. His approach has sought to take a structured approach to the development of practical agent systems from theoretical models. He has published over 70 articles in these areas and has published four books and edited collections.     

Learning formation control for fractional‐order multiagent systems

In this paper, we use 2 iterative learning control schemes (P‐type and PI‐type) with an initial learning rule to achieve the formation control of linear fractional‐order multiagent systems. To realize the finite‐time consensus, we assume repeatable operation environments as well as a fixed but directed communication topology for the fractional‐order multiagent systems. Both P‐type and PI‐type update laws are applied to generate the control commands for each agent. It is strictly proved that all agents are driven to achieve an asymptotical consensus as the iteration number increases. Two examples are simulated to verify the effectiveness of the proposed algorithms.     

Practical scaled consensus for nonlinear multiagent systems with input time delay via a new distributed integral-type event-triggered scheme

This paper studies scaled-based practical consensus issue for multiagent systems with input time delay by a fully continuous communication-free integral-type event-triggered scheme. By choosing the proper scales, scaled consensus can be induced to synchronization, bipartite consensus or cluster consensus. By defining a continuous communication-free measurement error for the integral-type event-triggered mechanism, the new integral-type event-triggered condition is proposed which can not only reduce the energy consumption but also prolong the interevent time. Then, with time domain analysis method, the distributed integral-type event-triggered control problem for nonlinear general multiagent systems involving input time delay is investigated and then the second-order counterpart, with a calculated upper-bound for time-delay. Moreover, it is concluded that with such event-triggered protocols, practical scaled consensus can be achieved without the exhibition of Zeno behavior. At last, simulations are shown to support the results.     

Dynamic output feedback consensus of continuous‐time networked multiagent systems

This article addresses the dynamic output feedback consensus problem of continuous‐time networked multiagent systems. Both a fixed topology and Markovian switching topologies are considered. The consensus algorithms are on the base of the output information of each agent's itself and its neighbors. Some sufficient conditions for consensus of multiagent systems are obtained in forms of bilinear matrix inequalities. The algorithm based on the homotopy continuation method is given to compute the feasible controller gains. Numerical simulations are given to show the effectiveness of the proposed results. © 2014 Wiley Periodicals, Inc. Complexity 20: 35–42, 2015     

Comparison of tightly and loosely coupled decision paradigms in multiagent expedition

Frameworks for cooperative multiagent decision making may be divided into those where each agent is assigned a single variable (SVFs) and those where each agent carries an internal model, which can be further divided into loosely coupled frameworks (LCFs) and tightly coupled frameworks (TCFs). In TCFs, agent communication interfaces render their subuniverses conditionally independent. In LCFs, either agents do not communicate or their messages are semantically less restricted. SVFs do not address the privacy issue well. LCF agents cannot draw from collective knowledge as well as TCF agents can. However, disproportional effort has been dedicated to SVFs and LCFs, which can be attributed partially to unawareness of the computational advantages of TCFs over performance, efficiency and privacy. This work aims to provide empirical evidence of such advantages by comparing recursive modeling method from LCFs and collaborative design network from TCFs, both of which are decision-theoretic and the latter of which is based on graphical models. We apply both to a testbed, multiagent expedition, resolve technical issues encountered, and report our experimental evaluation.     

PDα‐type distributed learning control for nonlinear fractional‐order multiagent systems

This paper considers the consensus tacking problem for nonlinear fractional‐order multiagent systems by presenting a PD^α‐type iterative learning control update law with initial learning mechanisms. The asymptotical convergence of the proposed distributed learning algorithm is strictly proved by using the properties of fractional calculus. A sufficient condition is derived to guarantee the whole multiagent system achieving an asymptotic output consensus. An illustrative example is given to verify the theoretical results.     

Distributed containment control of second‐order multiagent systems with input delays under general protocols

This article addresses the distributed containment control problem in a group of agents governed by second‐order dynamics with directed network topologies. Considering there are multiple leaders, we study a general second‐order containment controller which can realize several different consensus modes by adjusting control gains. A necessary and sufficient condition on the control gains of the general containment controller is provided. Moreover, the delay sensitivity of the closed‐loop multiagent system under the general containment controller is studied; the maximal upper bound of the constant delays is obtained. Finally, several numerical examples are used to illustrate the theoretical results. © 2015 Wiley Periodicals, Inc. Complexity 21: 112–120, 2016     

Consensus protocol design for discrete‐time networks of multiagent with time‐varying delay via logarithmic quantizer

This article considers the problem of consensus for discrete‐time networks of multiagent with time‐varying delays and quantization. It is assumed that the logarithmic quantizer is utilized between the information flow through the sensor of each agent, and its quantization error is included in the proposed method. By constructing a suitable Lyapunov‐Krasovskii functional and utilizing matrix theory, a new consensus criterion for the concerned systems is established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Based on the consensus criterion, a designing method of consensus protocol is introduced. One numerical example is given to illustrate the effectiveness of the proposed method. © 2014 Wiley Periodicals, Inc. Complexity 21: 163–176, 2015     

AgentGeom: a multiagent system for pedagogical support in geometric proof problems

This paper aims, first, to describe the fundamental characteristics and workings of the AgentGeom artificial tutorial system, which is designed to help students develop knowledge and skills related to problem solving, mathematical proof in geometry, and the use of mathematical language. Following this, we indicate the manner in which a secondary school student can appropriate these abilities through interactions with the system. Our system uses strategic messages of the agent tutor in an argumentative process that collaborates with a student in the construction of a proof.     

PdE-based model reference adaptive control of uncertain heterogeneous multiagent networks

Robust globally stable model reference adaptive control (MRAC) laws recently derived for systems described by parabolic and hyperbolic partial differential equations (PDEs) with spatially-varying coefficients under distributed sensing and actuation are extended to heterogeneous multiagent networks characterized by parameter uncertainty. The extension is carried out using partial difference equations (PdEs) on graphs that preserve parabolic- and hyperbolic-like cumulative network behavior. Unlike in the PDE case, only boundary input is specified for the reference model. The algorithms proposed directly incorporate this boundary reference input into the reference PdE to generate the distributed admissible reference evolution profile followed by the agents. The agent evolution thus depends only on the interaction with the adjacent agents, making the system fully decentralized. Numerical examples are presented as well, including the case of the switched topology associated with a sudden loss of an agent. The resulting PdE MRAC laws inherit the robust linear structure of their PDE counterparts.     

Reliable controller for nonlinear multiagent system with additive time varying delay and nonlinear actuator faults

In this paper, the problem of nonlinear multiagent system with reliable control is taken into account. The prescribed system consists of additive time-varying delay, actuator faults with both linear and nonlinear functions. The main focus of this paper is to design a reliable control which guarantees the stability and consensus condition of the proposed system. Actuator faults with linear and nonlinear functions are considered in the control input. From the implementation of integral inequality, the linear matrix inequality format is derived by constructing the suitable Lyapunov Krasovskii functional for the specified system. Terminally numerical examples are furnished for the efficiency of the specified method.     

An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization

Multiagent systems have been studied and widely used in the field of artificial intelligence and computer science to catalyze computation intelligence. In this paper, a multiagent evolutionary algorithm called RAER based on the ERA multiagent modeling pattern is proposed, where ERA has the same architecture as Swarm including three parts of Environment, Reactive rules and Agents. RAER integrates a novel roulette inversion operator (RIO) proposed in this paper and theoretically proved to conquer the irrationality of the inversion operator (IO) designed by John Holland when used for real code stochastic optimization algorithms. Experiments for numerical optimization of 4 benchmark functions show that the RIO operator bears better functioning than IO operator. And experiments for numerical optimization of 12 benchmark functions are used to examine the performance and scalability of RAER along the problem dimensions ranging 20-10 000, results indicate that RAER outperforms other comparative algorithms significantly. Also, two engineering optimization problems of a stable linear system approximation and a welded beam design are used to examine the applicability of RAER. Results show that RAER has better search ability and faster convergence speed. Especially for the approximation problem, REAR can find the proper optima belonging to different fixed search areas, which is significantly better than other algorithms and shows that RAER can search the problem domains more thoroughly than other algorithms. Hence, RAER is efficient and practical.     

Mapping deontic operators to abductive expectations

Deontic concepts and operators have been widely used in several fields where representation of norms is needed, including legal reasoning and normative multi-agent systems. The EU-funded SOCS project has provided a language to specify the agent interaction in open multi-agent systems. The language is equipped with a declarative semantics based on abductive logic programming, and an operational semantics consisting of a (sound and complete) abductive proof procedure. In the SOCS framework, the specification is used directly as a program for the verification procedure. In this paper, we propose a mapping of the usual deontic operators (obligations, prohibition, permission) to language entities, called expectations, available in the SOCS social framework. Although expectations and deontic operators can be quite different from a philosophical viewpoint, we support our mapping by showing a similarity between the abductive semantics for expectations and the Kripke semantics that can be given to deontic operators. The main purpose of this work is to make the computational machinery from the SOCS social framework available for the specification and verification of systems by means of deontic operators. Marco Alberti received his laurea degree in Electronic Engineering in 2001 and his Ph.D. in Information Engineering in 2005 from the University of Ferrara, Italy. His research interests include constraint logic programming and abductive logic programming, applied in particular to the specification and verification of multi-agent systems. He has been involved as a research assistants in national and European research projects. He currently has a post-doc position in the Department of Engineering at the University of Ferrara. Marco Gavanelli is currently assistant professor in the Department of Engineering at the University of Ferrara, Italy. He graduated in Computer Science Engineering in 1998 at the University of Bologna, Italy. He got his Ph.D. in 2002 at Ferrara University. His research interest include Artificial Intelligence, Constraint Logic Programming, Multi-criteria Optimisation, Abductive Logic Programming, Multi-Agent Systems. He is a member of ALP (the Association for Logic Programming) and AI*IA (the Italian Association for Artificial Intelligence). He has organised workshops, and is author of more than 30 publications between journals and conference proceedings. Evelina Lamma received her degree in Electronic Engineering from University of Bologna, Italy, in 1985 and her Ph.D. degree in Computer Science in 1990. Currently she is Full Professor at the Faculty of Engineering of the University of Ferrara where she teaches Artificial Intelligence and Foundations of Computer Science. Her research activity focuses around: – programming languages (logic languages, modular and object-oriented programming); – artificial intelligence; – knowledge representation; – intelligent agents and multi-agent systems; – machine learning. Her research has covered implementation, application and theoretical aspects. She took part to several national and international research projects. She was responsible of the research group at the Dipartimento di Ingegneria of the University of Ferrara in the UE ITS-2001-32530 Project (named SOCS), in the the context of the UE V Framework Programme - Global Computing Action. Paola Mello received her degree in Electronic Engineering from the University of Bologna, Italy, in 1982, and her Ph.D. degree in Computer Science in 1989. Since 1994 she has been Full Professor. She is enrolled, at present, at the Faculty of Engineering of the University of Bologna (Italy), where she teaches Artificial Intelligence. Her research activity focuses on programming languages, with particular reference to logic languages and their extensions, artificial intelligence, knowledge representation, expert systems with particular emphasis on medical applications, and multi-agent systems. Her research has covered implementation, application and theoretical aspects and is presented in several national and international publications. She took part to several national and international research projects in the context of computational logic. Giovanni Sartor is Marie-Curie professor of Legal informatics and Legal Theory at the European University Institute of Florence and professor of Computer and Law at the University of Bologna (on leave), after obtaining a PhD at the European University Institute (Florence), working at the Court of Justice of the European Union (Luxembourg), being a researcher at the Italian National Council of Research (ITTIG, Florence), and holding the chair in Jurisprudence at Queen’s University of Belfast (where he now is honorary professor). He is co-editor of the Artificial Intelligence and Law Journal and has published widely in legal philosophy, computational logic, legislation technique, and computer law. Paolo Torroni is Assistant Professor in computing at the Faculty of Engineering of the University of Bologna, Italy. He obtained a PhD in Computer Science and Electronic Engineering in 2002, with a dissertation on logic-based agent reasoning and interaction. His research interests mainly focus on computational logic and multi-agent systems research, including logic programming, abductive and hypothetical reasoning, agent interaction, dialogue, negotiation, and argumentation. He is in the steering committee of the CLIMA and DALT international workshops and of the Italian logic programming interest group GULP.     

Multiagent bayesian forecasting of structural time-invariant dynamic systems with graphical models

Time series are found widely in engineering and science. We study forecasting of stochastic, dynamic systems based on observations from multivariate time series. We model the domain as a dynamic multiply sectioned Bayesian network (DMSBN) and populate the domain by a set of proprietary, cooperative agents. We propose an algorithm suite that allows the agents to perform one-step forecasts with distributed probabilistic inference. We show that as long as the DMSBN is structural time-invariant (possibly parametric time-variant), the forecast is exact and its time complexity is exponentially more efficient than using dynamic Bayesian networks (DBNs). In comparison with independent DBN-based agents, multiagent DMSBNs produce more accurate forecasts. The effectiveness of the framework is demonstrated through experiments on a supply chain testbed.     

Hybrid Elections Broaden Complexity‐Theoretic Resistance to Control

Electoral control refers to attempts by an election's organizer (“the chair”) to influence the outcome by adding/deleting/partitioning voters or candidates. The important paper of Bartholdi, Tovey, and Trick [1] that introduces (constructive) control proposes computational complexity as a means of resisting control attempts: Look for election systems where the chair's task in seeking control is itself computationally infeasible. We introduce and study a method of combining two or more candidate‐anonymous election schemes in such a way that the combined scheme possesses all the resistances to control (i.e., all the NP‐hardnesses of control) possessed by any of its constituents: It combines their strengths. From this and new resistance constructions, we prove for the first time that there exists a neutral, anonymous election scheme (whose winner problem is computable in polynomial time) that is resistant to all twenty standard types of electoral control (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A test suite for the evaluation of mixed multi-unit combinatorial auctions

Mixed Multi-Unit Combinatorial Auctions extend and generalize all the preceding types of combinatorial auctions. In this paper, we try to make headway on the practical application of MMUCAs by: (1) providing an algorithm to generate artificial data that is representative of the sort of scenarios a winner determination algorithm is likely to encounter; and (2) subsequently assessing the performance of an Integer Programming implementation of MMUCA in CPLEX.     

Consensus problems in discrete-time multiagent systems with fixed topology

In this paper, we study the consensus problems in discrete-time multiagent systems with fixed topology. A necessary and sufficient condition for a system that solves a consensus problem is established, and the structure of consensus functions is characterized. Based on them, we introduce the standard topologies (graphs) of information flow, with which the systems can be viewed as single-leader-multi-follower systems. Moreover, the convex combination of these topologies can create a system that solves any predeterminate consensus problem. Additionally, we characterize the structural decomposition—the leaders-followers decomposition of a multiagent system, and establish a necessary and sufficient condition for an agent to be a leader.     

Heterogeneous trading agents

In this article, we present a multiagent system (MAS) simulation of a financial market and investigate the requirements to obtain realistic data. The model consists of autonomous, interactive agents that buy stock on a financial market. Transaction decisions are based on a number of individual and collective elements, the former being risk aversion and a set of decision rules reflecting their anticipation of the future evolution of prices and dividends and the latter the information arriving on the market influencing the decision making process of each trader. We specifically look at this process and the following observations hold: The market behavior is determined by the information arriving at the market and agent heterogeneity is required in order to obtain the right statistical properties of the price and return time series. The observed results are not sensitive to changes in the parameter values. © 2003 Wiley Periodicals, Inc.     

Self-regulation through social institutions: A framework for the design of open agent-based electronic marketplaces

In this paper, we argue that allowing self-interested agents to activate social institutions during runtime can improve the robustness (i.e., stability, reliability, or scalability) of open multiagent systems (MAS). Referring to sociological theory, we consider institutions to be rules that need to be activated and adopted by the agent population during runtime and propose a framework for self-regulation of MAS for the domain of electronic marketplaces. The framework consists of three different institutional types that are defined by the mechanisms and instances that generate, change or safeguard them. We suggest that allowing autonomous agents both the reasoning about their compliance with a rule and the selection of an adequate institutional types helps to balance the trade-off between the autonomy of self-interested agents and the maintenance of social order (cf. Castelfranchi, 2000) in MAS, and to ensure almost the same qualities as in closed environments. A preliminary report of the evaluation of the prototype by empirical simulations is given. Christian S. Hahn studied computer science and economics at Saarland University and received his diploma in 2004. Currently, he works in a project of the priority program ‘Socionics’ funded by the German Research Foundation at the German Research Center for Artificial Intelligence (DFKI). Bettina Fley studied sociology, economics, law, and social and economic history at the University of Hamburg and received her diploma in 2002. She currently works in a project in the priority program ‘Socionics’, which is funded by the German Research Foundation (DFG), at the Department of Technology Assessment at the Hamburg University of Technology. Michael Florian, received his master in sociology at the University of Münster, where he also finished his doctoral degree in 1993. Since 1995, he holds a position as a senior researcher (‘Oberingenieur’) at the Department of Technology Assessment at the Hamburg University of Technology and heads the sociological part of a project in the priority program ‘Socionics’ funded by the German Research Foundation (DFG).