scholarly journals THE HYBRID AGENT MODEL OF BEHAVIORAL TESTING

2015 ◽  
pp. 234-246
Author(s):  
Anna Sugak ◽  
Oleksandr Martynyuk ◽  
Oleksandr Drozd
Keyword(s):  
Diagnostic Tests ◽  
Behavioral Tests ◽  
Multi Agent Systems ◽  
Distributed Information ◽  
Agent Model ◽  
Multi Agent ◽  
Hybrid Agent ◽  
Agent Intelligence ◽  
Diagnostics System ◽  
Component Models

Operation testing and diagnostic tests, applied for distributed information systems, inherit and employ the properties of distribution, autonomy, goal formation and cooperation, natural for the multi-agent systems. This paper presents the behavioral diagnostics agent model, based on the evolutionary organization of component tests in the automata network environment. The model can be used to construct a multi-agent diagnostics system. A hybrid agent model provides a combination of reactive operation testing and deliberative diagnostic tests, based on the deterministic and evolutionary methods of synthesis of behavioral tests. An agent model consists of the component models of allocation environment, functioning goals and strategies, operations of observation, enforcement strategy and adaptation, initial component models, goals and strategies for ensuring the autonomy. Agent intelligence is based on a locally-exhaustive deterministic and pseudorandom targeted evolutionary synthesis of behavioral tests, providing and accumulating the results. Cooperation of the agents involves their deterministic and evolutionary interactions under the conditions of test feasibility and portability.

scholarly journals Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems

2019 ◽  
Vol 33 ◽  
pp. 7850-7857 ◽  
Author(s):  
Trong Nghia Hoang ◽  
Quang Minh Hoang ◽  
Kian Hsiang Low ◽  
Jonathan How
Keyword(s):  
Online Learning ◽  
Gaussian Processes ◽  
Message Passing ◽  
Transmission Loss ◽  
Streaming Data ◽  
Multi Agent Systems ◽  
Model Fusion ◽  
Agent Model ◽  
Massive Number ◽  
Multi Agent

This paper presents a novel Collective Online Learning of Gaussian Processes (COOL-GP) framework for enabling a massive number of GP inference agents to simultaneously perform (a) efficient online updates of their GP models using their local streaming data with varying correlation structures and (b) decentralized fusion of their resulting online GP models with different learned hyperparameter settings and inducing inputs. To realize this, we exploit the notion of a common encoding structure to encapsulate the local streaming data gathered by any GP inference agent into summary statistics based on our proposed representation, which is amenable to both an efficient online update via an importance sampling trick as well as multi-agent model fusion via decentralized message passing that can exploit sparse connectivity among agents for improving efficiency and enhance the robustness of our framework against transmission loss. We provide a rigorous theoretical analysis of the approximation loss arising from our proposed representation to achieve efficient online updates and model fusion. Empirical evaluations show that COOL-GP is highly effective in model fusion, resilient to information disparity between agents, robust to transmission loss, and can scale to thousands of agents.

scholarly journals DELIBERATIVE EVOLUTION IN MULTI-AGENT SYSTEMS

2001 ◽  
Vol 11 (05) ◽  
pp. 559-581 ◽  
Author(s):  
F. M. T. BRAZIER ◽  
C. M. JONKER ◽  
J. TREUR ◽  
N. J. E. WIJNGAARDS
Keyword(s):  
Multi Agent Systems ◽  
Agent Architecture ◽  
Individual Agent ◽  
New Agents ◽  
Agent Model ◽  
Development Method ◽  
Agent Systems ◽  
Generic Agent ◽  
Multi Agent ◽  
Material Process

Evolution of automated systems, in particular evolution of automated agents based on agent deliberation, is the topic of this paper. Evolution is not a merely material process, it requires interaction within and between individuals, their environments and societies of agents. An architecture for an individual agent capable of (1) deliberation about the creation of new agents, and (2) (run-time) creation of a new agent on the basis of this, is presented. The agent architecture is based on an existing generic agent model, and includes explicit formal conceptual representations of both design structures of agents and (behavioural) properties of agents. The process of deliberation is based on an existing generic reasoning model of design. The architecture has been designed using the compositional development method DESIRE, and has been tested in a prototype implementation.

scholarly journals The principles of synchronization in the distributed information systems

2020 ◽  
Vol 183 (5-6) ◽  
pp. 79-88
Author(s):  
Ivan Sinitsyn ◽  
◽  
Anton Mironov ◽  
Yuriy Vorontsov ◽  
Nikita Borzykh ◽  
...  
Keyword(s):  
Information System ◽  
Information Systems ◽  
Geographic Location ◽  
Data Synchronization ◽  
Multi Agent Systems ◽  
Distributed Information Systems ◽  
Distributed Information ◽  
Agent Systems ◽  
Multi Agent ◽  
Google Search

Information, especially its automated processing, is still an important factor in improving the efficiency of any organization. Distributed information systems (IS, ISs) differ from conventional ISs in architectural and infrastructural principles, as well as in the geographic location with integration into one information cluster. One of the examples of distributed information systems is the infrastructure of the Google search engine - more than 2,000 servers, with server bases in almost every country in the world, which allows achieving a minimum delay in sending and receiving client requests. A distributed information system can have a large number of different databases, both local and remote, with which constant data synchronization is required, while maintaining a backup copy of previous data in case of failures and emergency stops. Distributed information systems are highly reliable and require multi-level protection of the cluster from unauthorized access, adherence to the principles of data synchronization, which differ from a conventional information system. Within the framework of this paper, synchronization processes are investigated using mathematical and computational tools, creating an environment for distributed information systems. It is advisable to use the results of the work to coordinate the operation of components of multi-agent systems for various purposes, transmit messages between agents, build communication protocols, and provide conditions for self-organization of multi-agent systems.

Developing Smart Emergency Applications with Multi-Agent Systems

2010 ◽  
Vol 1 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Federico Bergenti ◽  
Agostino Poggi
Keyword(s):  
High Speed ◽  
Multi Agent Systems ◽  
Remote Collaboration ◽  
Distributed Information ◽  
Distributed Management ◽  
Agent Systems ◽  
Loosely Coupled ◽  
Critical Content ◽  
Multi Agent ◽  
Health Applications

Multi-agent systems have been importantly contributing to the development of the theory and the practice of complex distributed systems and, in particular, they have shown their potential to meet critical needs in high-speed, mission-critical, content-rich, distributed information applications where mutual interdependencies, dynamic environments, uncertainty, and sophisticated control play a remarkable role. Therefore, multi-agent systems are considered a suitable technology for the realization of e-health applications where the use of loosely coupled and heterogeneous components, the dynamic and distributed management of data, and the remote collaboration among users are often the most relevant requirements. This paper describes some of the main reasons why multi-agent systems are today considered one of the best technologies for the realization and deployment of advances for e-health applications and, in particular, of smart emergency applications. After an introduction on the inherent characteristics of the use of multi-agent systems for e-health, the paper presents the results of EU-scale project CASCOM: a real multi-agent system for the execution of smart emergency tasks.

Multi-Agent Systems for the Application and Employing of E-Health Services

2010 ◽  
pp. 247-264 ◽  
Author(s):  
Federico Bergenti ◽  
Agostino Poggi
Keyword(s):  
High Speed ◽  
Multi Agent Systems ◽  
Healthcare Applications ◽  
Remote Collaboration ◽  
Distributed Information ◽  
Agent Systems ◽  
Loosely Coupled ◽  
Critical Content ◽  
Multi Agent ◽  
Mission Critical

Multi-agent systems have been importantly contributing to the development of the theory and the practice of complex distributed systems and, in particular, have shown the potential to meet critical needs in high-speed, mission-critical content-rich and distributed information applications where mutual interdependencies, dynamic environments, uncertainty, and sophisticated control play a role. Therefore, multi-agent systems can be considered a suitable technology for the realization of healthcare applications where the use of loosely coupled and heterogeneous components, the dynamic and distributed management of data and the remote collaboration among users are often the most relevant requirements.

scholarly journals Fairness in multi-agent systems

2008 ◽  
Vol 23 (2) ◽  
pp. 153-180 ◽  
Author(s):  
STEVEN DE JONG ◽  
KARL TUYLS ◽  
KATJA VERBEECK
Keyword(s):  
Behavioral Economics ◽  
Computational Models ◽  
Computer Software ◽  
Multi Agent Systems ◽  
Agent Model ◽  
Agent Systems ◽  
Multi Agent ◽  
Classical Game ◽  
Descriptive Models ◽  
Classical Game Theory

AbstractMulti-agent systems are complex systems in which multiple autonomous entities, called agents, cooperate in order to achieve a common or personal goal. These entities may be computer software, robots, and also humans. In fact, many multi-agent systems are intended to operate in cooperation with or as a service for humans. Typically, multi-agent systems are designed assuming perfectly rational, self-interested agents, according to the principles of classical game theory. Recently, such strong assumptions have been relaxed in various ways. One such way is explicitly including principles derived from human behavior. For instance, research in the field of behavioral economics shows that humans are not purely self-interested. In addition, they strongly care aboutfairness. Therefore, multi-agent systems that fail to take fairness into account, may not be sufficiently aligned with human expectations and may not reach intended goals. In this paper, we present an overview of work in the area of fairness in multi-agent systems. More precisely, we first look at the classical agent model, that is, rational decision making. We then provide an outline of descriptive models of fairness, that is, models that explain how and why humans reach fair decisions. Then, we look at prescriptive, computational models for achieving fairness in adaptive multi-agent systems. We show that results obtained by these models are compatible with experimental and analytical results obtained in the field of behavioral economics.

scholarly journals Introduction to the special issue on ontologies in agent systems

2002 ◽  
Vol 17 (1) ◽  
pp. 1-5 ◽  
Author(s):  
STEPHEN CRANEFIELD ◽  
STEVEN WILLMOTT ◽  
TIM FININ
Keyword(s):  
Speech Acts ◽  
Coupled System ◽  
Knowledge Bases ◽  
Message Content ◽  
Multi Agent Systems ◽  
Distributed Information ◽  
Agent Systems ◽  
Loosely Coupled ◽  
Systems Research ◽  
Multi Agent

It is now more than ten years since researchers in the US Knowledge Sharing Effort envisaged a future where complex systems could be built by combining knowledge and services from multiple knowledge bases and the first agent communication language, KQML, was proposed (Neches et al., 1991). This model of communication, based on speech acts, a declarative message content representation language and the use of explicit ontologies defining the domains of discourse (Genesereth & Ketchpel, 1994), has become widely recognised as having great benefits for the integration of disparate and distributed information sources to form an open, extensible and loosely coupled system. In particular, this idea has become a key tenet in the multi-agent systems research community.

Intelligent Information Retrieval System Based on Muti-Agent Model

2013 ◽  
Vol 765-767 ◽  
pp. 1529-1532
Author(s):  
Zhao Dan Wu ◽  
Chang Feng Shi ◽  
Yi Lu
Keyword(s):  
Information Retrieval ◽  
Cognitive Theory ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Theoretical Derivation ◽  
Agent Model ◽  
Intelligent Information Retrieval ◽  
Multi Agent ◽  
Agent Intelligence ◽  
Intelligent Information

The intelligent information retrieval model discussed in this paper is constructed by multi-agent. Currently, BDI cognitive theory is accepted widely by the scholars of this field, but the related researches mainly focus on the theoretical derivation and presentation of symbols, lack of model facing practical application. In this article, a dynamic rule-based reasoning model is proposed. The model based on BDI theory is an expression of agent intelligence. The basic logical reasoning of the BDI theory is extended in this article. The author not only introduces several functions to study the dynamic changes of agents mental state, but also give a detailed description of how to use the theory of production rules to express BDI-based reasoning. This article also studies and designs the agent communication mechanism in MAS. Finally, the intelligent information retrieval system is designed and implemented with the idea of AOP.

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