Self-organizing multi-agent system for management and planning surveillance routes

This paper presents the THOMAS architecture, specially designed to model open multi-agent systems, and its application in the development of a multi-agent system for managing and planning surveillance routes for security personnel. THOMAS uses agents with reasoning and planning capabilities. These agents can perform a dynamic self-organization when they detect changes in the environment. THOMAS is appropriate for developing systems in highly dynamic environments similar to the one presented in this study, as demonstrated by the results obtained after having applied the system to a case study.Web of Science3151100108

Training Neural Networks for Financial Forecasting: Backpropagation vs Particle Swarm Optimization

Neural networks (NN) architectures can be effectively used to classify, forecast and recognize quantity of interest in, e.g., computer vision, machine translation, finance, etc. Concerning the financial framework, fore- casting procedures are often used as a part of the decision making process in both trading and portfolio strategy optimization. Unfortunately training a NN is in general a challenging task mainly because of the high number of parameters involved. In particular, a typical NN is based on a large number of layers, each of which may be composed by several neurons , moreover, for every component, normalization as well as training algorithms, have to be performed. One of the most popular method to overcome such difficulties is represented by the so called back propagation algorithm . Other possibilities are represented by genetic algorithms , and, in this family, the swarm particle optimization method seems to be rather promising. In this paper we want to compare canonical back- propagation and the swarm particle optimization algorithm in minimizing the error on surface created by financial time series, particularly concerning the task of forecast up/down movements for the assets we are interested in

Self-Organizing Multi-Agent System for Management and Planning Surveillance Routes

This paper presents the THOMAS architecture, specially designed to model open multi-agent systems, and its application in the development of a multi-agent system for managing and planning surveillance routes for security personnel. THOMAS uses agents with reasoning and planning capabilities. These agents can perform a dynamic self-organization when they detect changes in the environment. THOMAS is appropriate for developing systems in highly dynamic environments similar to the one presented in this study, as demonstrated by the results obtained after having applied the system to a case study

Optimal binary linear codes of dimension at most seven

AbstractWe classify optimal [n,k,d] binary linear codes of dimension ⩽7, with one exception, where by optimal we mean that no [n−1,k,d],[n+1,k+1,d], or [n+1,k,d+1] code exists. In particular, we present (new) classification results for codes with parameters [40,7,18], [43,7,20], [59,7,28], [75,7,36], [79,7,38], [82,7,40], [87,7,42], and [90,7,44]. These classifications are accomplished with the aid of the first author's computer program Extension for extending from residual codes, and the second author's program Split

Modelling Agents Cooperation Through Internal Visions of Social Network and Episodic Memory

Human societies appear in many types of simulations. Particularly, a lot of new computer games contain a virtual world that imitates the real world. A few of the most important and the most difficult society elements to be modelled are the social context and individuals cooperation. In this paper we show how the social context and cooperation ability can be provided using agents that are equipped with internal visions of mutual social relations. Internal vision is a representation of social relations from the agent's point of view so, due to being subjective, it may be inconsistent with the reality. We introduce the agent model and the mechanism of rebuilding the agent's internal vision that is similar to that used by humans. An experimental proof of concept implementation is also presented

Exploiting the Use of Cooperation in Self-Organizing Reliable Multiagent Systems

In this paper, a novel and cooperative approach is exploited introducing a self-organizing engine to achieve high reliability and availability in multiagent systems. The Adaptive Multiagent Systems theory is applied to design adaptive groups of agents in order to build reliable multiagent systems. According to this theory, adaptiveness is achieved via the cooperative behaviors of agents and their ability to change the communication links autonomously. In this approach, there is not a centralized control mechanism in the multiagent system and there is no need of global knowledge of the system to achieve reliability. This approach was implemented to demonstrate its performance gain in a set of experiments performed under different operating conditions. The experimental results illustrate the effectiveness of this approach

A Novel Way of Using Simulations to Support Urban Security Operations

The growing importance of security operations in urban terrain has triggered many attempts to address the perceived gaps in the readiness of security forces for this type of combat. One way to tackle the problem is to employ simulation techniques. Simulations are widely used to support both mission rehearsal and mission analysis, but these two applications tend to be seen as distinctly separate. We argue that integrating them in a unified framework can bring significant benefits for end-users. We perform a structured walk-through of such a unified system, in which a novel approach to integration through the behaviour cloning enabled the system to capture the operational knowledge of security experts, which is often difficult to express verbally. This capability emerged as essential for the operation of the integrated system. We also illustrate how the interplay between the system components for the mission analysis and mission rehearsal is realized

How to Deal with Unbelievable Assertions

We tackle the problem that arises when an agent receives unbelievable information. Information is unbelievable if it conflicts with the agent’s convictions, that is, what the agent considers knowledge. We propose two solutions based on modifying the information so that it is no longer unbelievable. In one solution, the source and the receiver of the information cooperatively resolve the conflict. For this purpose we introduce a dialogue protocol in which the receiver explains what is wrong with the information by using logical interpolation, and the source produces a new assertion accordingly. If such cooperation is not possible, we propose an alternative solution in which the receiver revises the new piece of information by its own convictions to make it acceptable.Peer reviewe