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These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the Semantic Web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This 15th issue contains extended and revised versions of the best papers presented at the International Conference on Practical Applications on Agents and Multi-Agent Systems (PAAMS 2012 and PAAMS 2013) held in Salamanca, Spain.



Building Optimal Macroscopic Representations of Complex Multi-agent Systems

Application to the Spatial and Temporal Analysis of International Relations Through News Aggregation
The design and the debugging of large-scale MAS require abstraction tools in order to work at a macroscopic level of description. Agent aggregation provides such abstractions by reducing the complexity of the system’s microscopic representation. Since it leads to an information loss, such a key process may be extremely harmful for the analysis if poorly executed. This paper presents measures inherited from information theory to evaluate abstractions and to provide the experts with feedback regarding the quality of generated representations. Several evaluation techniques are applied to the spatial and temporal aggregation of an agent-based model of international relations. The information from on-line newspapers constitutes a complex microscopic representation of the agent states. Our approach is able to evaluate geographical abstractions used by the domain experts in order to provide efficient and meaningful macroscopic representations of the world global state.
Robin Lamarche-Perrin, Yves Demazeau, Jean-Marc Vincent

Understanding the Role of Emotions in Group Dynamics in Emergency Situations

Decision making under stressful circumstances, e.g., during evacuation, often involves strong emotions and emotional contagion from others. In this paper the role of emotions in social decision making in large technically assisted crowds is investigated. For this a formal, computational model is proposed, which integrates existing neurological and cognitive theories of affective decision making. Based on this model several variants of a large scale crowd evacuation scenario were simulated. By analysis of the simulation results it was established that (1) human agents supported by personal assistant devices are recognised as leaders in groups emerging in evacuation; (2) spread of emotions in a crowd increases the resistance of agent groups to opinion changes; (3) spread of emotions in a group increases its cohesiveness; (4) emotional influences in and between groups are, however, attenuated by personal assistant devices, when their number is large.
Alexei Sharpanskykh, Kashif Zia

Representation of the Agent Environment for Traffic Behavioral Simulation

The aim of this paper is to improve the validity of traffic simulations in (sub-)urban context, with a better consideration of driver behavior in terms of anticipation of positioning on the lanes and occupation of space. We introduce a model based on a multi-agent approach and the emergence concept. This model considers that each driver perceives the situation in an ego-centered way and readapts the road space using the virtual lane concept. We implement the model with the traffic simulation tool ArchiSim. The so obtained simulator intends to reproduce the observed behavior such as filtering between vehicles (two-wheels, emergency vehicles), repositioning on lanes when approaching the road intersections and “exceptional” situations (stranded vehicle or improperly parked, etc.).
Feirouz Ksontini, Stéphane Espié, Zahia Guessoum, René Mandiau

Using LCS to Exploit Order Book Data in Artificial Markets

In the study of financial phenomena, multi-agent market order-driven simulators are tools that can effectively test different economic assumptions. Many studies have focused on the analysis of adaptive learning agents carrying on prices. But the prices are a consequence of the matching orders. Reasoning about orders should help to anticipate future prices.
While it is easy to populate these virtual worlds with agents analyzing “simple” prices shapes (rising or falling, moving averages, ...), it is nevertheless necessary to study the phenomena of rationality and influence between agents, which requires the use of adaptive agents that can learn from their environment. Several authors have obviously already used adaptive techniques but mainly by taking into account prices historical. But prices are only consequences of orders, thus reasoning about orders should provide a step ahead in the deductive process.
In this article, we show how to leverage information from the order books such as the best limits, the bid-ask spread or waiting cash to adapt more effectively to market offerings. Like B. Arthur, we use learning classifier systems and show how to adapt them to a multi-agent system.
Philippe Mathieu, Yann Secq

Coupled K-Nearest Centroid Classification for Non-iid Data

Most traditional classification methods assume the independence and identical distribution (iid) of objects, attributes and values. However, real world data, such as multi-agent data and behavioral data, usually contains strong couplings among values, attributes and objects, which greatly challenges existing methods and tools. This work targets the coupling similarities from these three perspectives and designs a novel classification method that applies a weighted K-Nearest Centroid to obtain the coupled similarity for non-iid data. From value and attribute perspectives, coupled similarity serves as a metric for nominal objects, which consider not only intra-coupled similarity within an attribute but also inter-coupled similarity between attributes. From the object perspective, we propose a more effective method that measures the centroid object by connecting all related objects. Extensive experiments on UCI and student data sets reveal that the proposed method outperforms classical methods for higher accuracy, especially in imbalanced data.
Mu Li, Jinjiu Li, Yuming Ou, Ya Zhang, Dan Luo, Maninder Bahtia, Longbing Cao

An Adaptative Multi-Agent System to Co-construct an Ontology from Texts with an Ontologist

Ontologies are one of the most used representations to model the domain knowledge. An ontology consists of a set of concepts connected by semantic relations. The construction and evolution of an ontology are complex and time-consuming tasks. This paper presents DYNAMO-MAS, an Adaptive Multi-Agent System (AMAS) that automates these tasks by co-constructing an ontology from texts with an ontologist. Terms and concepts of a given domain are agentified and they act, according to the AMAS approach, by solving the non cooperative situations they locally perceive at runtime. These agents cooperate to determine their position in the AMAS (that is the ontology) thanks to (i) lexical relations between terms, (ii) some adaptive mechanisms enabling addition, removing or moving of new terms, of concepts and of relations in the ontology as well as (iii) feedbacks from the ontologist about the propositions given by the AMAS. This paper focuses on the instantiation of the AMAS approach to this difficult problem. It presents the architecture of DYNAMO-MAS, and details the cooperative behaviors of the two types of agents we defined for ontology evolution. Finally evaluations made on three different ontologies are given in order to show the genericity of our solution.
Zied Sellami, Valérie Camps

Game Theoretical Model for Adaptive Intrusion Detection System

We present a self-adaptation mechanism for network intrusion detection system based on the use of game-theoretical formalism. The key innovation of our method is a secure runtime definition and solution of the game and real-time use of game solutions for immediate system reconfiguration. Our approach is suited for realistic environments where we typically lack any ground truth information regarding traffic legitimacy/maliciousness and where the significant portion of system inputs may be shaped by the attacker in order to render the system ineffective. Therefore, we rely on the concept of challenge insertion: we inject a small sample of simulated attacks into the unknown traffic and use the system response to these attacks to define the game structure and utility functions. This approach is also advantageous from the security perspective, as the manipulation of the adaptive process by the attacker is far more difficult.
Jan Stiborek, Martin Grill, Martin Rehak, Karel Bartos, Jan Jusko

+Cloud: A Virtual Organization of Multiagent System for Resource Allocation into a Cloud Computing Environment

Nowadays Cloud Computing has gained in importance at a remarkable pace. The key characteristic of this technology is the possibility to provide new resources to the services in an elastic way according to current demand. In contrast to Cloud Computing, Multiagent Systems are focus on other features such as autonomy, decentralization, auto-organization, etc. This study demonstrates that this features of MAS are suitable to manage the physical infrastructure of a Cloud Computing environment, in other words, we present +Cloud which is a cloud platform managed by a Multiagent System.
Fernando De la Prieta, Sara Rodríguez, Javier Bajo, Juan M. Corchado


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