scholarly journals An Approach to Model Based Testing of Multiagent Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Shafiq Ur Rehman ◽  
Aamer Nadeem
Keyword(s):  
Multiagent Systems ◽  
Autonomous Agents ◽  
Multiagent System ◽  
Dynamic Environment ◽  
Proactive Behavior ◽  
Prototype Tool ◽  
Novel Approach ◽  
Model Based Testing ◽  
Coverage Criterion ◽  
Prometheus Methodology

Autonomous agents perform on behalf of the user to achieve defined goals or objectives. They are situated in dynamic environment and are able to operate autonomously to achieve their goals. In a multiagent system, agents cooperate with each other to achieve a common goal. Testing of multiagent systems is a challenging task due to the autonomous and proactive behavior of agents. However, testing is required to build confidence into the working of a multiagent system. Prometheus methodology is a commonly used approach to design multiagents systems. Systematic and thorough testing of each interaction is necessary. This paper proposes a novel approach to testing of multiagent systems based on Prometheus design artifacts. In the proposed approach, different interactions between the agent and actors are considered to test the multiagent system. These interactions include percepts and actions along with messages between the agents which can be modeled in a protocol diagram. The protocol diagram is converted into a protocol graph, on which different coverage criteria are applied to generate test paths that cover interactions between the agents. A prototype tool has been developed to generate test paths from protocol graph according to the specified coverage criterion.

scholarly journals Debugging agent programs with "why?" Questions

2020 ◽  
Author(s):  
Michael Winikoff
Keyword(s):  
Multiagent Systems ◽  
Autonomous Agents ◽  
Dynamic Environment ◽  
Cognitive Agent ◽  
Failure Handling ◽  
Dramatic Difference ◽  
Execution Model ◽  
Why Questions

© Copyright 2017, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All Rights Reserved. Debugging is hard, and debugging cognitive agent programs is particularly hard, since they involve concurrency, a dynamic environment, and a complex execution model that includes failure handling. Previous work by Ko & Myers has demonstrated that providing Alice and Java programmers with software that can answer "why?" and "why not?" questions can make a dramatic difference to debugging performance. This paper considers how to adapt this approach to cognitive agent programs, specifically AgentSpeak. It develops and formalises definitions for "why?" and "why not?" questions and associated answers, and illustrates their application using a scenario.

scholarly journals Debugging agent programs with "why?" Questions

2020 ◽  
Author(s):  
Michael Winikoff
Keyword(s):  
Multiagent Systems ◽  
Autonomous Agents ◽  
Dynamic Environment ◽  
Cognitive Agent ◽  
Failure Handling ◽  
Dramatic Difference ◽  
Execution Model ◽  
Why Questions

© Copyright 2017, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All Rights Reserved. Debugging is hard, and debugging cognitive agent programs is particularly hard, since they involve concurrency, a dynamic environment, and a complex execution model that includes failure handling. Previous work by Ko & Myers has demonstrated that providing Alice and Java programmers with software that can answer "why?" and "why not?" questions can make a dramatic difference to debugging performance. This paper considers how to adapt this approach to cognitive agent programs, specifically AgentSpeak. It develops and formalises definitions for "why?" and "why not?" questions and associated answers, and illustrates their application using a scenario.

scholarly journals A Novel Clustering Method Based on Quasi-Consensus Motions of Dynamical Multiagent Systems

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Ning Cai ◽  
Chen Diao ◽  
M. Junaid Khan
Keyword(s):  
Multiagent Systems ◽  
Multiagent System ◽  
Theoretical Foundation ◽  
Cluster Structure ◽  
Group Consensus ◽  
Necessary And Sufficient Condition ◽  
Graph Topology ◽  
Novel Approach ◽  
Similar Cluster ◽  
Necessary And Sufficient

This paper presents a novel approach for clustering, which is based on quasi-consensus of dynamical linear high-order multiagent systems. The graph topology is associated with a selected multiagent system, with each agent corresponding to one vertex. In order to reveal the cluster structure, the agents belonging to a similar cluster are expected to aggregate together. To establish the theoretical foundation, a necessary and sufficient condition is given to check the achievement of group consensus. Two numerical instances are furnished to illustrate the results of our approach.

scholarly journals Eigenvalue Based Approach for Global Consensus in Multiagent Systems with Nonlinear Dynamics

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Wei Qian ◽  
Lei Wang
Keyword(s):  
Nonlinear Dynamics ◽  
Multiagent Systems ◽  
Multiagent System ◽  
Symmetric Matrix ◽  
Algebraic Connectivity ◽  
Communication Graph ◽  
Consensus Protocol ◽  
Global Consensus ◽  
The Common ◽  
Analytical Result

This paper addresses the global consensus of nonlinear multiagent systems with asymmetrically coupled identical agents. By employing a Lyapunov function and graph theory, a sufficient condition is presented for the global exponential consensus of the multiagent system. The analytical result shows that, for a weakly connected communication graph, the algebraic connectivity of a redefined symmetric matrix associated with the directed graph is used to evaluate the global consensus of the multiagent system with nonlinear dynamics under the common linear consensus protocol. The presented condition is quite simple and easily verified, which can be effectively used to design consensus protocols of various weighted and directed communications. A numerical simulation is also given to show the effectiveness of the analytical result.

scholarly journals MULTIAGENT SYSTEM OF FORECASTING OF CHAOTIC PROCESSES ON THE BASIS OF STATISTICAL EXTRAPOLATORS

2021 ◽  
Vol 56 ◽  
pp. 65-71
Author(s):  
Alexander A. Musaev ◽  
◽  
Andrey V. Gaikov ◽  
Keyword(s):  
Multiagent Systems ◽  
Chaotic System ◽  
Multiagent System ◽  
Intelligent Agent ◽  
System Component ◽  
Stationary Processes ◽  
Retrospective Data ◽  
System State ◽  
Generation Algorithm

The problem of the of a non-stationary system state predicting is considered. The decision based on the joint processing of the results obtained by a group of independent statistical extrapolators. In the terminology of multiagent systems, each extrapolator is an intelligent agent. The quality of the agent solutions is evaluated on retrospective data and is used as weight characteristic in the problem of a terminal solution estimation. The specificity of non-stationary processes with a chaotic system component leads to the empiricca version of the forecast generation algorithm

Means-ends based know-how mapping

2017 ◽  
Vol 21 (2) ◽  
pp. 454-473 ◽  
Author(s):  
Arnon Sturm ◽  
Daniel Gross ◽  
Jian Wang ◽  
Eric Yu
Keyword(s):  
Graduate Students ◽  
Design Methodology ◽  
Expert Knowledge ◽  
Design Science ◽  
Content Type ◽  
Know How ◽  
Knowledge Domains ◽  
Prototype Tool ◽  
Novel Approach ◽  
Practical Implications

Purpose The purpose of this paper is to report on research that aims to make knowledge, and in particular know-how, more easily accessible to both academic and industrial communities, as well as to the general public. The paper proposes a novel approach to map out know-how information, so all knowledge stakeholders are able to contribute to the knowledge and expertise accumulation, as well as using that knowledge for research and applying expertise to address problems. Design/methodology/approach This research followed a design science approach in which mapping of the know-how information was done by the research team and then tested with graduate students. During this research, the mapping approach was continuously evaluated and refined, and mapping guidelines and a prototype tool were developed. Findings Following an evaluation with graduate students, it was found that the know-how maps produced were easy to follow, allowed continuous evolution, facilitated easy modification through provided modularity capabilities, further supported reasoning about know-how and overall provided adequate expressiveness. Furthermore, we applied the approach with various domains and found that it was a good fit for its purpose across different knowledge domains. Practical implications This paper argues that mapping out know-how within research and industry communities can further improve resource (knowledge) utilization, reduce the phenomena of “re-inventing the wheel” and further create linkage across communities. Originality/value With the qualities mentioned above, know-how maps can both ease and support the increase of access to expert knowledge to various communities, and thus, promote re-use and expansion of knowledge for various purposes. Having an explicit representation of know-how further encourages innovation, as knowledge from various domains can be mapped, searched and reasoned, and gaps can be identified and filled.

On Using Multiagent Systems for Spectrum Sharing in Cognitive Radios Networks

2011 ◽  
pp. 377-415 ◽  
Author(s):  
Usama Mir ◽  
Leila Merghem-Boulahia ◽  
Dominique Gaïti
Keyword(s):  
Multiagent Systems ◽  
Spectrum Sharing ◽  
Multiagent System ◽  
Cognitive Radios ◽  
Dynamic Spectrum ◽  
Spectrum Allocation ◽  
Wireless Devices ◽  
Spectrum Utilization ◽  
Dynamic Spectrum Sharing ◽  
Theoretical Approaches

In modern day wireless networks, spectrum utilization and allocation are static. Generally, static spectrum allocation is not a feasible solution considering the distributed nature of wireless devices, thus some alternatives must be ensured in order to allocate spectrum dynamically and to mitigate the current spectrum scarcity. An effective solution to this problem is cognitive radio (CR), which seeks the empty spectrum portions and shares them with the neighboring devices. The CR devices can utilize the available spectrum more efficiently if they try to work together. Therefore, in this work, we review a number of dynamic spectrum allocation techniques, especially those using multiagent systems and game-theoretical approaches, and investigate their applicability to CR networks. The distributed nature of these two domains makes them suitable for CR networks. In fact, the idea of dynamic spectrum sharing using these techniques is not entirely new and several interesting approaches already exist in literature. Thus, in our study we try to focus on existing spectrum sharing literature and cooperative multiagent system for CR networks. We are particularly interested in showing how the distributed nature of multiagent system can be combined with cognitive radios in order to alleviate the current static spectrum usage as well as maintaining cooperation amongst the CR nodes. Moreover, our work includes the description of various scenarios in which spectrum sharing is an essential factor and hence must be performed in a dynamic and opportunistic manner. We also explain the working of our proposed spectrum allocation approach using multiagent system cooperation in one of these scenarios and verify its formal behavior using Petri net modeling.

Engineering of Experience Based Trust for E-Commerce

2010 ◽  
pp. 342-367 ◽  
Author(s):  
Zhaohao Sun ◽  
Jun Han ◽  
Dong Dong ◽  
Shuliang Zhao
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Research And Development ◽  
Multiagent Systems ◽  
System Architecture ◽  
Intelligent Systems ◽  
Multiagent System ◽  
Theoretical Foundation ◽  
Knowledge Based ◽  
Trust Inference

Trust is significant for sustainable development of e-commerce and has received increasing attention in e-commerce, multiagent systems (MAS), and artificial intelligence (AI). However, little attention has been given to the theoretical foundation and intelligent techniques for trust in e-commerce from a viewpoint of intelligent systems and engineering. This chapter will fill this gap by examining engineering of experience-based trust in e-commerce from the viewpoint of intelligent systems. It looks at knowledgebased trust, inference-based trust and their interrelationships with experience-based trust. It also examines scalable trust in e-commerce. It proposes a knowledge based model of trust in e-commerce and a system architecture for METSE: a multiagent system for experience-based trust in e-commerce. The proposed approach in this chapter will facilitate research and development of trust, multiagent systems, e-commerce and e-services.

scholarly journals Multiagent System for Mutual Collaboration Classification for Cancer Detection

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Mais Haj Qasem ◽  
Amjad Hudaib ◽  
Nadim Obeid
Keyword(s):  
Cancer Detection ◽  
Autonomous Agents ◽  
Multiagent System ◽  
Distributed Data ◽  
Distributed Clustering ◽  
Classification Tasks ◽  
Communication And Coordination ◽  
Prediction Techniques ◽  
Distributed Classification ◽  
And Behavior

A multiagent system (MAS) is a mechanism for creating goal-oriented autonomous agents in shared environments with communication and coordination facilities. Distributed data mining benefits from this goal-oriented mechanism by implementing various distributed clustering, classification, and prediction techniques. Hence, this study developed a novel multiagent model for distributed classification tasks in cancer detection with the collaboration of several hospitals worldwide using different classifier algorithms. A hospital agent requests help from other agents for instances that are difficult to classify locally. The agents communicate their beliefs (calculated classification), and others decide on the benefit of using such beliefs in classifying instances and adjusting their prior assumptions on each class of data. A MAS model state and behavior and communication are then developed to facilitate information sharing among agents. Regarding accuracy, implementing the proposed approach in comparison with typically different noncommunicated distributed classifications shows that sharable information considerably increases the classification task accuracy by 25.77%.

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