Ebook: Advanced Methods and Technologies for Agent and Multi-Agent Systems

This volume contains the proceedings of the 7th KES Conference on Agent and Multi-Agent Systems – Technologies and Applications (KES-AMSTA 2013) held on May 27–29, 2013 in Hue city, Vietnam. The conference was organized by KES International and its Focus Group on Agent and Multi-agent Systems, and Hue University, Vietnam.

Following the success of previous KES Symposia/Conferences on Agent and Multi-Agent Systems – Technologies and Applications, held in Wroclaw, Poland (KES-AMSTA 2007), Incheon, Korea (KES-AMSTA 2008), Uppsala, Sweden (KES-AMSTA 2009), Gdynia, Poland (KES-AMSTA 2010), Manchester, UK (KES-AMSTA 2011), and Dubrovnik, Croatia (KES-AMSTA 2012), this conference continues to provide an internationally respected forum for scientific research in the technologies and applications of agent and multi-agent systems.

The field of agent and multi-agent systems is concerned with the development and evaluation of sophisticated, AI-based problem-solving and control architectures for both single-agent and multi-agent systems. Current topics of research in the field include, among other, agent-oriented software engineering, beliefs engineering, desires and intentions representation, agent co-operation, co-ordination, negotiation, organization and communication, distributed problem solving, specification of agent communication languages, formalization of ontologies and conversational agents. Special attention is paid to the feature topics: Intelligent technologies and applications in the area of e-health, social networking, self-organizing systems, economics and trust management.

KES-AMSTA 2013 features a number of keynote talks, oral presentations, and invited sessions, closely aligned to the theme of the conference. The conference attracted a substantial number of researchers and practitioners from all over the world who submitted their papers for the main track covering the methodology and applications of agent and multi-agent systems, two special sessions on specific topics within the field, and a half day workshop for early career stage researchers.

The main track streams, covering the methodology and applications of agents and multi-agent systems, includes sessions on: Multi-Agent Systems Design and Implementation, Agent-Based Modeling and Simulation, Coordination, Cooperation and Teamwork, Agent-Based Optimization, Web Services and Semantic Web, Agent Theories, Models and Communication, and Social and Business Issues.

In addition to the main tracks of the conference two invited sessions are hosted: Intelligent Agents with Semantic Technology (IAST 2013), and Computational Intelligence for Business Collaboration (CIBC 2013). On the final day of the conference the workshop entitled New Directions in Agents Research is held.

Submissions to KES-AMSTA 2013 came from 20 countries. Each paper was peer reviewed by at least two members of the International Programme Committee and International Reviewer Board. In all, 44 best papers were selected for oral presentation and publication in the proceedings volume of KES-AMSTA 2013.

The papers presented during the conference highlight new trends and challenges in agent and multi-agent research. We hope that these results will be of value to the research community working in the fields of artificial intelligence, collective computational intelligence, robotics, dialogue systems and, in particular, agent and multi-agent systems, technologies and applications.

We would like to express our sincere thanks to the KES-AMSTA 2013 General Chair and KES-AMSTA Symposium Series and Focus Group on Agent and Multi-agent Systems Chair, Ngoc Thanh Nguyen from Wroclaw University of Technology, Poland. Since the first KES-AMSTA conference in 2007, each year his work and involvement in the organization of KES-AMSTA series conferences has contributed to ensure high quality of these events.

We are very grateful to the keynote speakers, Andrzej Szalas, University of Warsaw, Poland, and Arkady Zaslavsky, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia, for their interesting and informative talks of the world-class standard.

Our thanks are due to all International Programme Committee members for their valuable efforts in the review process, which helped us to guarantee the highest quality of selected papers for the conference. Thanks are also due to several additional reviewers, who contributed to the conference success. Our special gratitude is also directed to the Invited Session Chair, Linh Anh Nguyen, and organizers and chairs of invited sessions, Trong Hai Duong, Jason J. Jung, and Hanh H. Hoang, for their valuable contribution.

We extend our thanks to main organizers and sponsors, KES International and Hue University, Vietnam. Our special thanks go to the Organizing Chair, Hanh H. Hoang, and all members of Local Organizing Committee for their excellent efforts in the organizational work.

Finally, we cordially thank all the authors for their valuable contributions and the other participants in this conference. The conference would not have been possible without their support.

March 2013

Dariusz Barbucha

Manh Thanh Le

Robert J. Howlett

Lakhmi C. Jain

Agents' beliefs can be incomplete and partially inconsistent. The process of agents' belief formation in such contexts has to be supported by suitable tools allowing one to express a variety of inconsistency resolving and nonmonotonic reasoning techniques.

In this paper we discuss 4QL*, a general purpose rule-based query language allowing one to use rules with negation in the premises and in the conclusions of rules. It is based on a simple and intuitive semantics and provides uniform tools for lightweight versions of well-known forms of nonmonotonic reasoning. In addition, it is tractable w.r.t. data complexity and captures PTIME queries, so can be used in real-world applications.

Reasoning in 4QL* is based on well-supported models. We simplify and at the same time generalize previous definitions of well-supported models and develop a new algorithm for computing such models.

Despite many years of work on multi-agent platforms development, the problem of their performance remains unsolved. Even most mature solutions still suffer from significant limitations in terms of message-passing services and the number of agents which can work simultaneously. Large overheads are caused mostly by unsuitability of technology used for implementing the agent platform itself. In this paper several measures of an agent platform performance are proposed. Two popular agent platforms are thoroughly tested and compared to Erlang technology. The results of the experiments show that different approach to the problem of creating agent platform can give significantly better performance.

Because of congestions in traffic a precomputed shortest route (path) of a vehicle from an initial location to a prescribed one cannot be always adhered. In such a case it is necessary to change it flexibly. However, to save cost the new route should be chosen in virtue of prescribed rules. The place/transition Petri nets, more precisely its simpler kind named as state machines (SM), is utilized here to model the real structure of the possible routes area. An algorithm for finding the least-cost firing sequence of SM transitions is proposed. To extend the approach on adjacent areas the agent-based approach is drawn too.

This paper proposes a scalable, multi-agent architecture for control of modern outdoor lighting systems. Most contemporary lighting systems utilize a static control structure, which is based on simple criteria (e.g. date, time of day, weather forecast) and operate on few (two or three) lighting modes of luminaries. Thus, centralized management is sufficient for such systems. Modern lighting control systems take dynamic and fine-grained (often local) conditions into account and operate on more sophisticated equipment, characterized by flexible lighting levels and geometries. These factors cause scalability problems, which may render the system unable to react to incoming events in time. The proposed solution introduces a hierarchy of agents, which allow for distributed control and supervision. Moreover a graph-based model is introduced as a formal representation of the agent's knowledge, which allows it to be processed in parallel by distributed agents.

We propose a wolf-caribou predator-prey system to verify our hypothesis that relatively complex collective escaping behavior may emerge from simple, implicit, locally defined, and therefore scalable interactions between the caribou (prey) agents. Proposing two different communication mechanisms – (i) simple, basic mechanism of implicit interaction, and (ii) explicit communications promoting the awareness of the caribou about the identity of the chased one (i.e., empathy), we present a comparative analysis of the implications of these communication mechanisms on the efficiency of evolution of the emerged collective behavior. We used strongly typed genetic programming with exception handling capabilities to evolve the collective behavior of caribou agents. The experimental results suggest that the empathy facilitates the evolution of collective escaping behavior of the team of caribou agents..

Multi-agent computations are a useful computing paradigm applied in various areas, such as smart grids or distributed information processing. Another field of their application is outdoor lighting design, which is characterized by highly time-consuming optimization tasks. In this paper we focus on the latter case, aiming at scalability and reducing the computing time, which are crucial for solving the lighting design problem. To improve the efficiency of parallel, agent-based computations the results, a reuse approach is introduced. A quantitative comparison with other approaches is also included.

Despite its extensive use in emerging Service Science research, value co-creation in complex service systems still remains highly conceptual and ambiguous. Therefore, there exists a need of tools and techniques to model complex service systems making the process of value co-creation operational. Agent-Based Modeling and Simulation has been recognized as a powerful technique in studying complex adaptive systems and hence, becomes a potential approach in studying complex value co-creation interactions and emerging properties of service systems. However, the computational representation of the value co-creation process among agents of a complex service system still remains challenging. This paper proposes a candidate method to address this issue based on Kauffman's NKCS theoretical framework that mimics the co-evolution of multiple species in a biological ecosystem. The key aspects of value co-creation have been discussed using a toy example of two agents. Furthermore the extendibility of the method to model complex service systems has also been discussed.

Transport systems are more and more complex and have to evolve to integrate more connected entities (mobile devices, localized vehicles, etc.). It becomes critical to develop micro-simulation tools for mobility policies makers taking into account this fact. In this paper, we propose a multimodal travel simulator that allows for the understanding and the prediction of future status of the networks and allows for testing new online applications. The application simulates the movements of travelers on the different networks while taking into account the changes in travel times and the status of the networks. The considered transport modes include pedestrians, private cars, all public transport modes and ridesharing. The simulator has been developed using the Repast Simphony^® multiagent simulation platform.

Multi-level agent-based modeling (ML-ABM) requires representing agents at different levels of representation in the same model w.r.t. to time, space and behavior. This paper describes a generic and operational proposal for ML-ABM. First, a generic meta-model for ML-ABM and an associated “morphogenesis” operation are introduced. The generic meta-model allows a modeler to describe multiple levels of representation in the same model, while the “morphogenesis” operation supports agent change of representation level dynamically during the course of the simulation. Second, in order to demonstrate how to operationalize the proposal, we present an implementation of the generic meta-model and the “morphogenesis” operation in the GAMA ABM platform. Finally, we illustrate how our proposal, implemented in the GAMA platform, allows modeler in practice to develop a multi-level agent-based model. To do so, we rely on the famous “Boids” model of Craig Reynolds and show how the modeler can easily introduce a new level of representation of an entity to transform the model into a two-levels agent-based model without having to modify the existing model.

Finding an optimal design for the environmental surveillance network is a realistic need for any ecosystem manager. There are two main factors related to the optimization of a surveillance network: number of sampling points (deciding the sampling density) and locations of such sampling points. This paper aims at proposing an agent-based model to add k measuring devices into a current surveillance network. The simulation is used to verify multiple possibilities of a heterogeneous environment. A Correlation & Disk graph-based Surveillance Network (CDSN) is also implemented and then optimized. Gaussian process is used to model the measuring data and different covariance functions (e.g. variogram in geostatistics). The experimental results of the model are performed for the insect monitoring in Mekong Delta region, Vietnam.

Currently, research in normative multi-agent systems focus on how a visitor or new agent detects and updates its host norms autonomously without being explicitly given by the host system. In this paper, we present our proposed algorithm to detect the obligation and prohibition norms which we called the Obligation and Prohibition Norms Mining algorithm (OPNM). The algorithm exploits the resources of the host system, implements data formatting, filtering, and extracting the exceptional events, i.e. those that entail rewards and penalties of the obligation and prohibition norms and identifies the ensuing normative protocol. In this work, we assume that an agent is aware of its environment and is able to reason about its surrounding events. We then demonstrate the operation of the algorithm by applying it on a typical scenario and analyzing the results.

We propose an efficient team formation method for multi-agent systems consisting of self-interested agents in task-oriented domains. Services computing on computer networks have been rapidly increasing. Efficient team formation for service tasks is considered to be a way to improve performance. Our method is based on our previous parameter learning method enabling agents to efficiently form teams but requiring prior knowledge about all others' resources. We extended that method by adding a resource estimation method so as to increase its applicability to actual application systems. We experimentally evaluated our method by comparing it with the previous method and the task allocation using contract net protocol (CNP). The results demonstrated that the proposed method outperformed other methods even though it did not require prior knowledge about resources in other agents. We discuss the reason for this improvement.

This paper describes a multi-agent system (MAS) acting on an arterial road network, where each intersection is controlled by an agent. The agents are concerned with the efficient control of their intersection. In order to improve the overall performance of the system we propose a model of explicit coordination that directs the behavior of agents for the formation of green waves on the artery, in addition to maintaining the autonomy of each agent. The model is tested in simulation and compared with the traditional approach of synchronization of traffic lights. The results obtained in simulation overcome the traditional model by representing a more realistic model of traffic.

This paper is to introduce the new concept of coalition Nash equilibrium of a strategic game, and to show that a communication among the players in a coalition leads to the equilibrium through messages. A coalition Nash equilibrium for a strategic game consists of (1) a subset S of players, (2) independent mixed strategies for each member of S, (3) the conjecture of the actions for the other players not in S with the condition that each member of S maximises his/her expected payoff according to the product of all mixed strategies for S and the other players' conjecture. However, this paper stands on the Bayesian point of view as follows: The players start with the same prior distribution on a state-space. In addition they have private information which is given by a partition of the state space. Each player in a coalition S predicts the other players' actions as the posterior of the others' actions given his/her information. He/she communicates privately their beliefs about the other players' actions through messages among all members in S according to the communication network in S, which message is information about his/her individual conjecture about the others' actions. The recipients update their belief by the messages. Precisely, at every stage each player communicates privately not only his/her belief about the others' actions but also his/her rationality as messages according to a protocol and then the recipient updates their private information and revises her/his prediction. In this circumstance, we show that the conjectures of the players in a coalition S regarding the future beliefs converge in the long run communication, which lead to a coalition Nash equilibrium for the strategic game.

Population-based metaheuristics, mostly inspired by biological or social phenomena, belong to a widely used class of approaches suitable for solving complex hard optimization problems. Their effectiveness has been confirmed for many real-time instances of different optimization problems. This paper proposes an Agent-Based Cooperative Population Learning Algorithm for the Vehicle Routing Problem with Time Windows, where the search for solutions is divided into stages, and different learning/improvement procedures are used at each stage. These procedures are based on a set of heuristics (represented as software agents) which are run under the cooperation schemma defined separately for each stage. Computational experiment, which has been carried out, confirmed the effectiveness of the proposed approach.

Resource allocation is one of the principal stages of query processing in relational data grid systems. Specific characteristics of the data grid environment, such as dynamicity, heterogeneity and large scale, impose serious restrictions to the resource allocation process. Static resource allocation before the query execution may be far from optimal due to the dynamic changes of the system. One possible optimization is to adjust dynamically the allocation of resources during the query execution. Some methods of dynamic resource allocation have been proposed, however, most of them use centralized control mechanisms. In this study we argue that the decentralized approach meets better the requirements of the data grid systems. In this study we propose a decentralized method of dynamic resource allocation that is based on the mobile agent paradigm. We consider the participating nodes as autonomous and independent elements of the system, each of which can detect if it is overloaded and make the decision to react. Then we consider each relational operation as a mobile agent running on the allocated node, meaning that, it keeps track of its own status and can migrate to another node at any time. A two-level cooperation mechanism between such autonomous nodes and autonomous operations is described in detail. Performance evaluation proves the efficiency of the proposed method.

In the paper an application of hybridized Evolutionary Multi-Agent System (EMAS) with local search (in memetic style) to the problem of continuous optimisation is presented. Before, the concept of evolutionary and memetic agent-based computing is given, the former being a computing paradigm researched for over 15 years, the latter being introduced recently. Two ways of memetic hybridization (Lamarckian and Baldwinian) are discussed, and examined in the course of experiments. In the presented experiments, evolutionary and memetic multi-agent systems are compared with classical evolutionary algorithm (Michalewicz model) implemented with allopatric speciation (island-model of evolutionary algorithm), based on a selected popular benchmark continuous optimization functions.

Linked Open Data (LOD) is a successfully initiative Semantic Web, which uses URIs and RDF techniques to connect related pieces of data on the Semantic Web. A rapidly growing number of LODs leads to new challenges for query processing. SPARQL-endpoint has been proposed to access LODs through SPARQL language, however, it requires a holistic understanding of the data when making a SPARQL query and supports a limited number of related LODs. To access distributed LODs, the LOD-federation is proposed. The LOD-federation is an abstract layer consisting a federated ontology and mechanisms for user query processing across LODs via the corresponding endpoints. In this study, a technical survey of recent works supporting for the LOD-federation building, is presented. An effective method of LOD ontology integration is suggested in order to build the federated ontology. Query decomposition algorithm is depicted to translate a query to the LOD-federation into subsequent LODs. Result integration method using co-reference entity and join operations, is also presented.