Intelligent Software Agents and Multi-Agent Systems

2011 ◽  
pp. 2126-2131
Author(s):  
Milan Stankovic ◽  
Uros Krcadinac ◽  
Vitomir Kovanovic ◽  
Jelena Jovanovic
Keyword(s):  
Research And Development ◽  
Traffic Control ◽  
Autonomous Agents ◽  
Point Of View ◽  
Object Oriented Programming ◽  
Mobile Systems ◽  
Critical Systems ◽  
Multi Agent Systems ◽  
Autonomous Action ◽  
Intelligent Software Agents

Agent-based systems are one of the most important and exciting areas of research and development that emerged in information technology (IT) in the past two decades. In a nutshell, an agent is a computer program that is capable of performing a flexible, autonomous action in typically dynamic and unpredictable domains (Luck, McBurney, Shehory, & Willmott, 2005). Agents emerged as a response of the IT research community to the new data-processing requirements that traditional computing models and paradigms were increasingly incapable to deal with (e.g., the huge and ever-increasing quantities of available data). Many IT researchers believe that agents represent one of the most important software paradigms that have emerged since the object orientation. From the historic point of view, the agent-oriented research and development (R&D) originates from different disciplines. Undoubtedly, the main contribution to the field of autonomous agents came from artificial intelligence (AI). Ultimately, AI is all about building intelligent artifacts and if these artifacts sense and act in some environment, then they can be considered agents (Russell & Norvig, 1995). Also, object-oriented programming (Booch, 2004), concurrent object-based systems (Agha, Wegner, & Yonezawa, 1993), and human-computer interaction (Maes, 1994) are fields that constantly drive forward the agent R&D in the last few decades. In addition, the concept of an agent has become important in a diverse range of sub-disciplines of IT, including software engineering, computer networks, mobile systems, control systems, decision support, information retrieval and management, electronic commerce, and many others. Agents are being used in an increasingly wide variety of applications— ranging from comparatively small systems such as personalized email filters to large, complex, mission critical systems such as air-traffic control.

Intelligent Multi-Agent Systems

2009 ◽  
pp. 464-469
Author(s):  
Uros Krcadinac ◽  
Milan Stankovic ◽  
Vitomir Kovanovic ◽  
Jelena Jovanovic
Keyword(s):  
Autonomous Agents ◽  
Research Community ◽  
Object Oriented Programming ◽  
Multi Agent Systems ◽  
Autonomous Action ◽  
Agent Systems ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Object Based ◽  
Multi Agent

Since the AAAI (http://www.aaai.org) Spring Symposium in 1994, intelligent software agents and agentbased systems became one of the most significant and exciting areas of research and development (R&D) that inspired many scientific and commercial projects. In a nutshell, an agent is a computer program that is capable of performing a flexible, autonomous action in typically dynamic and unpredictable domains (Luck, McBurney, Shehory, & Willmott, 2005). Agents emerged as a response of the IT research community to the new data-processing requirements that traditional computing models and paradigms were increasingly incapable to deal with (e.g., the huge and ever-increasing quantities of available data). Agent-oriented R&D has its roots in different disciplines. Undoubtedly, the main contribution to the field of autonomous agents came from artificial intelligence (AI) which is focused on building intelligent artifacts; and if these artifacts sense and act in some environment, then they can be considered agents (Russell & Norvig, 1995). Also, object-oriented programming (Booch, 2004), concurrent object-based systems (Agha, Wegner, & Yonezawa, 1993), and human-computer interaction (Maes, 1994) are fields that have constantly driven forward the agent R&D in the last few decades.

Intelligent Software Agents with Applications in Focus

2011 ◽  
pp. 950-955
Author(s):  
Mario Jankovic-Romano ◽  
Milan Stankovic ◽  
Uroš Krcadinac
Keyword(s):  
Traffic Control ◽  
Software Agents ◽  
Real Life ◽  
Object Oriented Programming ◽  
Mobile Systems ◽  
Multi Agent Systems ◽  
Information Recovery ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Multi Agent

Most people are familiar with the concept of agents in real life. There are stock-market agents, sports agents, real-estate agents, etc. Agents are used to filter and present information to consumers. Likewise, during the last couple of decades, people have developed software agents, that have the similar role. They behave intelligently, run on computers, and are autonomous, but are not human beings. Basically, an agent is a computer program that is capable of performing a flexible and independent action in typically dynamic and unpredictable domains (Luck, McBurney, Shehory, & Willmott, 2005). Agents are capable of performing actions and making decisions without the guidance of a human. Software agents emerged in the IT because of the ever-growing need for information processing, and the problems concerning dealing and working with large quantities of data. Especially important is how agents act with other agents in the same environment, and the connections they form to find, refine and present the information in a best way. Agents certainly can do tasks better if they perform together, and that is why the multi-agent systems were developed. The concept of an agent has become important in a diverse range of sub-disciplines of IT, including software engineering, networking, mobile systems, control systems, decision support, information recovery and management, e-commerce, and many others. Agents are now used in an increasingly wide number of applications — ranging from comparatively small systems such as web or e-mail filters to large, complex systems such as air-traffic control, that have a large dependency on fast and precise decision making. Undoubtedly, the main contribution to the field of intelligent software agents came from the field of artificial intelligence (AI). The main focus of AI is to build intelligent entities and if these entities sense and act in some environment, then they can be considered agents (Russell & Norvig, 1995). Also, object-oriented programming (Booch, 2004), concurrent object-based systems (Agha, Wegner, and Yonezawa, 1993), and human- computer interaction (Maes, 1994) are fields that constantly drive forward the development of agents.

An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game

2011 ◽  
pp. 77-98
Author(s):  
Malgorzata Lucinska ◽  
Slawomir T. Wierzchon
Keyword(s):  
Learning Strategies ◽  
Autonomous Agents ◽  
Clonal Selection ◽  
Hybrid Approach ◽  
Point Of View ◽  
Distributed Programming ◽  
Multi Agent Systems ◽  
Pursuit Evasion ◽  
Multi Agent ◽  
Complex Relationships

Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they will require the ability to cooperate, coordinate, and negotiate with each other. From a theoretical point of view such systems require a hybrid approach involving game theory, artificial intelligence, and distributed programming. On the other hand, biology offers a number of inspirations showing how these interactions are effectively realized in real world situations. Swarm organizations, like ant colonies or bird flocks, provide a spectrum of metaphors offering interesting models of collective problem solving. Immune system, involving complex relationships among antigens and antibodies, is another example of a multi-agent and swarm system. In this chapter an application of so-called clonal selection algorithm, inspired by the real mechanism of immune response, is proposed to solve the problem of learning strategies in the pursuit-evasion problem.

An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game

2012 ◽  
pp. 1192-1214
Author(s):  
Malgorzata Lucinska ◽  
Slawomir T. Wierzchon
Keyword(s):  
Learning Strategies ◽  
Autonomous Agents ◽  
Clonal Selection ◽  
Hybrid Approach ◽  
Point Of View ◽  
Distributed Programming ◽  
Multi Agent Systems ◽  
Pursuit Evasion ◽  
Multi Agent ◽  
Complex Relationships

Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they will require the ability to cooperate, coordinate, and negotiate with each other. From a theoretical point of view such systems require a hybrid approach involving game theory, artificial intelligence, and distributed programming. On the other hand, biology offers a number of inspirations showing how these interactions are effectively realized in real world situations. Swarm organizations, like ant colonies or bird flocks, provide a spectrum of metaphors offering interesting models of collective problem solving. Immune system, involving complex relationships among antigens and antibodies, is another example of a multi-agent and swarm system. In this chapter an application of so-called clonal selection algorithm, inspired by the real mechanism of immune response, is proposed to solve the problem of learning strategies in the pursuit-evasion problem.

scholarly journals Comparison of Direct and Adjoint k and α-Eigenfunctions

2021 ◽  
Vol 2 (2) ◽  
pp. 132-151
Author(s):  
Vito Vitali ◽  
Florent Chevallier ◽  
Alexis Jinaphanh ◽  
Andrea Zoia ◽  
Patrick Blaise
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Energy Transport ◽  
Distinct Point ◽  
Point Of View ◽  
Critical Systems ◽  
Small Deviations ◽  
Power Iteration ◽  
Continuous Energy ◽  
Fission Probability

Modal expansions based on k-eigenvalues and α-eigenvalues are commonly used in order to investigate the reactor behaviour, each with a distinct point of view: the former is related to fission generations, whereas the latter is related to time. Well-known Monte Carlo methods exist to compute the direct k or α fundamental eigenmodes, based on variants of the power iteration. The possibility of computing adjoint eigenfunctions in continuous-energy transport has been recently implemented and tested in the development version of TRIPOLI-4®, using a modified version of the Iterated Fission Probability (IFP) method for the adjoint α calculation. In this work we present a preliminary comparison of direct and adjoint k and α eigenmodes by Monte Carlo methods, for small deviations from criticality. When the reactor is exactly critical, i.e., for k0 = 1 or equivalently α0 = 0, the fundamental modes of both eigenfunction bases coincide, as expected on physical grounds. However, for non-critical systems the fundamental k and α eigenmodes show significant discrepancies.

scholarly journals A Programming Approach to Collective Autonomy

2021 ◽  
Vol 10 (2) ◽  
pp. 27
Author(s):  
Roberto Casadei ◽  
Gianluca Aguzzi ◽  
Mirko Viroli
Keyword(s):  
Autonomous Vehicles ◽  
Autonomic Computing ◽  
Autonomous Agents ◽  
Programming Approach ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Simulated Case ◽  
Complex Settings

Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.

scholarly journals Evolution of coordination in pairwise and multi-player interactions via prior commitments

2021 ◽  
pp. 105971232199316
Author(s):  
Ndidi Bianca Ogbo ◽  
Aiman Elragig ◽  
The Anh Han
Keyword(s):  
Autonomous Agents ◽  
Evolutionary Game ◽  
Multi Agent Systems ◽  
Coordination Problem ◽  
Mutual Cooperation ◽  
Payoff Structure ◽  
Optimal Coordination ◽  
Selection Intensities ◽  
Collective Benefit ◽  
High Level

Upon starting a collective endeavour, it is important to understand your partners’ preferences and how strongly they commit to a common goal. Establishing a prior commitment or agreement in terms of posterior benefits and consequences from those engaging in it provides an important mechanism for securing cooperation. Resorting to methods from Evolutionary Game Theory (EGT), here we analyse how prior commitments can also be adopted as a tool for enhancing coordination when its outcomes exhibit an asymmetric payoff structure, in both pairwise and multi-party interactions. Arguably, coordination is more complex to achieve than cooperation since there might be several desirable collective outcomes in a coordination problem (compared to mutual cooperation, the only desirable collective outcome in cooperation dilemmas). Our analysis, both analytically and via numerical simulations, shows that whether prior commitment would be a viable evolutionary mechanism for enhancing coordination and the overall population social welfare strongly depends on the collective benefit and severity of competition, and more importantly, how asymmetric benefits are resolved in a commitment deal. Moreover, in multi-party interactions, prior commitments prove to be crucial when a high level of group diversity is required for optimal coordination. The results are robust for different selection intensities. Overall, our analysis provides new insights into the complexity and beauty of behavioural evolution driven by humans’ capacity for commitment, as well as for the design of self-organised and distributed multi-agent systems for ensuring coordination among autonomous agents.

Financial support for universities in the top of Project 5–100 and its impact on the quality of education

2021 ◽  
Vol 23 (2) ◽  
pp. 83-87
Author(s):  
ALEXANDER KVASHIN ◽  
Keyword(s):  
Research And Development ◽  
Financial Management ◽  
Global Market ◽  
Point Of View ◽  
Educational Services ◽  
Ministry Of Education ◽  
Financial Condition ◽  
University Funding ◽  
Russian Universities

Analysis of the main trends in the development of the market for higher education services, affecting the transformation of university financing models, shows that improving the quality of educational services in Russian universities directly depends on an increase in the share of revenues received from research and development, as well as the ability of universities to present the results of their research and development in the form of a complete product from a marketing point of view and build a competent strategy for promoting innovations in the market. In the context of creating a long-term strategy for increasing the competitive advantages of leading universities, the author pays special attention to Project 5–100 of the Ministry of Education and Science of the Russian Federation, the purpose of which is to maximize the competitive position of a group of leading Russian universities in the global market of educational services and research programs. It is noted that university funding comes from various sources, while budgetary revenues dominate the structure of income, and the reduction in budgetary provision significantly affects the financial condition of Russian universities. The author comes to the conclusion that the forms and mechanisms of financial management of universities and research organizations are not strictly regulated, they independently choose the sources of funding.

Сase-systems for development of multiagent system (MAS) in the system of diagnostics and maintenance of vehicles

2021 ◽  
Vol 13 (1) ◽  
pp. 87-93
Author(s):  
Viacheslav Pavlenko ◽  
◽  
Volodymyr Manuylov ◽  
Volodymyr Kuzhel ◽  
◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Technical Condition ◽  
Point Of View ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Software Products ◽  
Multi Agent ◽  
Software Code ◽  
Low Performance ◽  
Intelligent Methods

The article provides a comparative analysis of existing software products and libraries that allow the design of multi-agent systems for diagnostics and maintenance systems for modern cars. The authors substantiate two main shortcomings inherent in all products - analogues: the need for high qualification of the user as a software code developer, and low performance of intelligent methods in the structure of agents, which worsens their performance. Both manufacturers and car owners are objectively interested in the widespread use of telematics systems for monitoring the technical condition of cars. Predictive diagnostics gives them access to a huge amount of information about all the nuances of car operation, wherever they are, during the entire service life. The application of this approach using a multi-agent system (MAS) will allow taking the next step in this direction. Information from the connected vehicles goes to the main server. Systematization and analysis of data make it possible to establish the causes of malfunctions, identify patterns of their occurrence and make further predictions. Purpose of the work: to perform a comparative analysis of existing software products and libraries that allow the design of multi-agent systems. The topic of the work is disclosed on the example of the analysis of software tools for the development and design of MAS at the present stage. There are a number of systems and libraries on the market designed for the development of multi-agent systems. These Case - systems are suitable for the development of multi-agent systems of any direction, that is, they are universal from this point of view. Therefore, for us it is a universal application tool for the automotive industry. Ultimately, the work performed a comparative analysis of existing software products and libraries that allow the design of multi-agent systems. The analysis revealed two main drawbacks inherent in all products - analogs: the need for high user qualifications as a developer of software code, and low indicators of the possibilities of introducing intelligent methods into the structure of agents, worsens the indicators of their work.

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