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  Browse All Reviews > Computing Methodologies (I) > Artificial Intelligence (I.2) > Distributed Artificial Intelligence (I.2.11) > Multiagent Systems (I.2.11...)  
 
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  1-10 of 165 Reviews about "Multiagent Systems (I.2.11...)": Date Reviewed
  A multiagent, dynamic rank-driven multi-deme architecture for real-valued multiobjective optimization
Acan A., Lotfi N.  Artificial Intelligence Review 48(1): 1-29, 2017. Type: Article

Solutions for multiobjective optimization problems find use in architectures that support parallel processing. A new method proposed by Acan and Lotfi is seen to dominate over most other optimization solutions, as is the case with a typical Pareto...

Nov 1 2017
  Fast rendezvous on a cycle by agents with different speeds
Feinerman O., Korman A., Kutten S., Rodeh Y.  Theoretical Computer Science 688 77-85, 2017. Type: Article

Feinerman et al. provide illustrations concerned with the usefulness of different processing speeds and asynchrony of tasks of the operating agents in distributed computing systems. To investigate the rendezvous (meeting point) problem, a cycle to...

Oct 16 2017
  Introducing the oscillations based paradigm: the simulation of agents and social systems
Plikynas D.,  Springer International Publishing, New York, NY, 2016. 325 pp. Type: Book (978-3-319390-39-0)

OSIMAS is based on three conceptual models: the oscillating agent model (OAM), the wave-like interaction mechanism (WIM), and the pervasive information field (PIF). This book has been prepared and produced as one of the results of the OSIMAS resea...

Sep 20 2017
  Markov chain aggregation for agent-based models
Banisch S.,  Springer International Publishing, New York, NY, 2015. 195 pp. Type: Book (978-3-319248-75-2)

Most techniques for modeling dynamic systems fall into one of two categories. Equation-based models such as system dynamics and other differential equation formalisms seek a closed-form expression for the overall dynamics, but typically characteri...

Jul 12 2017
  Computational models of motivation for game-playing agents
Merrick K.,  Springer International Publishing, New York, NY, 2016. 213 pp. Type: Book (978-3-319334-57-8)

One of the major dimensions in which current computer models of players in a gaming system differ from actual human players is affective aspects. Creating a computational model of motivation that can be implemented in such computer players blurs t...

Jun 22 2017
  Optimizing transport logistics processes with multiagent planning and control
Gath M.,  Springer Vieweg, Wiesbaden, Germany, 2016. 279 pp. Type: Book (978-3-658140-02-1)

Modern logistics brings various challenging and complex problems to researchers’ attention. Among them, planning and control problems need efficient problem-solving approaches; multiagent systems are known as the most state-of-the-art artifi...

Apr 26 2017
  Multiagent resource allocation for dynamic task arrivals with preemption
Doucette J., Pinhey G., Cohen R.  ACM Transactions on Intelligent Systems and Technology 8(1): 1-27, 2016. Type: Article

The authors describe a new approach to multiagent resource allocation problems. A typical problem would be the allocation of resources in an emergency room situation as new cases arrive continually. The model consists of four components: tasks, ta...

Sep 15 2016
  Agent-based modelling in economics
Hamill L., Gilbert N.,  Wiley Publishing, Chichester, UK, 2016. 256 pp. Type: Book (978-1-118456-07-1)

The social sciences have long been plagued by physics envy. The seductive simplicity of closed-form equations with strong predictive power in dealing with billiard balls, planets, and electrons suggests that we ought to be able to analyze the beha...

Aug 25 2016
  Artificial intelligence applications in information and communication technologies
Laalaoui Y., Bouguila N.,  Springer International Publishing, New York, NY, 2015. 213 pp. Type: Book (978-3-319198-32-3)

According to analysts, data volumes are expected to grow 44 fold in a decade (2011-2020). The variety of the data, along with the speed with which it is generated in an increasingly connected world, makes artificial intelligence (AI)-driven soluti...

Jul 8 2016
  Exponential moving average based multiagent reinforcement learning algorithms
Awheda M., Schwartz H.  Artificial Intelligence Review 45(3): 299-332, 2016. Type: Article

Reinforcement learning for multiagent systems aims to find optimal policies that can be learned by agents during their interaction in cooperative or competitive games. In game theory, the target is reaching the Nash equilibrium, where each agent i...

Jun 15 2016
 
 
 
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