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Über dieses Buch

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 thirty-second issue presents 5 selected papers in the field of management, economics and computer science.

Inhaltsverzeichnis

Frontmatter

Consensus Theory for Cognitive Agents’ Unstructured Knowledge Conflicts Resolving in Management Information Systems

Abstract
Management information systems of distributed nature, play a vital role in any kind of business organizations’ activity. The multi-agent systems, based on cognitive agent architecture, deserve special attention in this class of systems. They allow not only to access to the information and quick search for interesting us information, its analysis and drawing conclusions, but also, in addition to responding to stimuli from the environment, have the cognitive ability to learning through empirical experience gained through direct interaction with the environment. It, in turn, allows for the automatic generation of variants of decisions and, in many cases, even taking and putting into action the decisions. The big problem currently, however, turns out to be the processing of unstructured knowledge in systems of this kind. In contemporary companies, unstructured knowledge is essential, mainly due to the possibility to obtain better flexibility and competitiveness of the organization. Therefore, unstructured knowledge supports structured knowledge to a high degree. Simultaneously, one must note that the most prevailing phenomenon is a conflict in unstructured knowledge. It is extremely difficult to resolve conflicts of this kind properly. However, it is also very important, since it can improve the operation of management information system and, consequently, help the organization that employs the system become more flexible and competitive.
The main aim of this work is to develop a formal method to resolve conflicts in unstructured knowledge of cognitive agents in management information systems employing the consensus theory. The first part of this work presents an analysis problems related to management information systems and unstructured knowledge processing in these systems. Next, the cognitive agents are characterized with particular emphasis on unstructured knowledge processing. The use of consensus theory in unstructured knowledge conflicts resolving have been characterized in the third part of the work. The last part presents the developed method for cognitive agents’ knowledge conflicts resolving. The correctness of the method was verified using the prototypes of the agents helping to invest in the Forex market and processing user opinions about products and services.
Marcin Hernes

The Ins and Outs of Network-Oriented Modeling: From Biological Networks and Mental Networks to Social Networks and Beyond

Abstract
Network-Oriented Modeling has successfully been applied to obtain network models for a wide range of phenomena, including Biological Networks, Mental Networks, and Social Networks. In this paper it is discussed how the interpretation of a network as a causal network and taking into account dynamics in the form of temporal-causal networks, brings more depth. The basics and the scope of applicability of such a Network-Oriented Modelling approach are discussed and illustrated. This covers, for example, Social Network models for social contagion or information diffusion, adaptive Mental Network models for Hebbian learning and adaptive Social Network models for evolving relationships. From the more fundamental side, it will be discussed how emerging network behavior can be related to network structure.
Jan Treur

Local Termination Criteria for Swarm Intelligence: A Comparison Between Local Stochastic Diffusion Search and Ant Nest-Site Selection

Abstract
Stochastic diffusion search (SDS) is a global Swarm Intelligence optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Although population based decision mechanisms employed by many Swarm Intelligence methods can suffer poor convergence resulting in ill-defined halting criteria and loss of the best solution, as a result of its resource allocation mechanism, the solutions found by Stochastic Diffusion Search enjoy excellent stability.
Previous implementations of SDS have deployed stopping criteria derived from global properties of the agent population; this paper examines new local SDS halting criteria and compares their performance with ‘quorum sensing’ (a termination criterion naturally deployed by some species of tandem-running ants). In this chapter we discuss two experiments investigating the robustness and efficiency of the new local termination criteria; our results demonstrate these to be (a) effectively as robust as the classical SDS termination criteria and (b) almost three times faster.
Andrew O. Martin, J. Mark Bishop, Elva J. H. Robinson, Darren R. Myatt

Towards Large-Scale Optimization of Iterated Prisoner Dilemma Strategies

Abstract
The Iterated Prisoner’s Dilemma (IPD) game is a one of the most popular subjects of study in game theory. Numerous experiments have investigated many properties of this game over the last decades. However, topics related to the simulation scale did not always play a significant role in such experimental work. The main contribution of this paper is the optimization of IPD strategies performed in a distributed actor-based computing and simulation environment. Besides showing the scalability and robustness of the framework, we also dive into details of some key simulations, analyzing the most successful strategies obtained.
Grażyna Starzec, Mateusz Starzec, Aleksander Byrski, Marek Kisiel-Dorohinicki, Juan C. Burguillo, Tom Lenaerts

GuruWS: A Hybrid Platform for Detecting Malicious Web Shells and Web Application Vulnerabilities

Abstract
Web application/service is now omnipresent but its security risks, such as malware and vulnerabilities, are indeed underestimated. In this paper, we propose a protective, extensible and hybrid platform, named GuruWS, for automatically detecting both web application vulnerabilities and malicious web shells. Based on the original PHP vulnerability scanner THAPS, we propose E-THAPS which implements a novel detection mechanism, an improved SQL injection, Cross-site Scripting and vulnerability detection capabilities. For malicious web shell detection, taint analysis and pattern matching methods are chosen to be implemented in GuruWS. A number of extensive experiments are carried out to prove the outstanding performance of our proposed platform in comparison with several existing solutions in detecting either web application vulnerabilities or malicious web shells.
Van-Giap Le, Huu-Tung Nguyen, Duy-Phuc Pham, Van-On Phung, Ngoc-Hoa Nguyen

Backmatter

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