Browsing by Subject "Reinforcement learning"

Now showing items 1-15 of 15

    • Article  

      Achieving reliability in master-worker computing via evolutionary dynamics 

      Christoforou, Evgenia; Fernández Anta, Antonio; Georgiou, Chryssis; Mosteiro, Miguel A.; Sánchez, A. (2012)
      This work considers Internet-based task computations in which a master process assigns tasks, over the Internet, to rational workers and collect their responses. The objective is for the master to obtain the correct task ...
    • Article  

      Artificial neural network learning: A comparative review 

      Neocleous, Costas K.; Schizas, Christos N. (2002)
      Various neural learning procedures have been proposed by different researchers in order to adapt suitable controllable parameters of neural network architectures. These can be from simple Hebbian procedures to complicated ...
    • Article  

      Behavioral plasticity through the modulation of switch neurons 

      Vassiliades, Vassilis; Christodoulou, Chris C. (2016)
      A central question in artificial intelligence is how to design agents capable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by ...
    • Article  

      Crowd Computing as a Cooperation Problem: An Evolutionary Approach 

      Christoforou, Evgenia; Fernández Anta, Antonio; Georgiou, Chryssis; Mosteiro, Miguel A.; Sánchez, A. (2013)
      Cooperation is one of the socio-economic issues that has received more attention from the physics community. The problem has been mostly considered by studying games such as the Prisoner's Dilemma or the Public Goods Game. ...
    • Article  

      An extension of a hierarchical reinforcement learning algorithm for multiagent settings 

      Lambrou, Ioannis; Vassiliades, Vassilis; Christodoulou, Chris C. (2012)
      This paper compares and investigates single-agent reinforcement learning (RL) algorithms on the simple and an extended taxi problem domain, and multiagent RL algorithms on a multiagent extension of the simple taxi problem ...
    • Article  

      Internet computing: Using reputation to select workers from a pool 

      Christoforou, Evgenia; Fernández Anta, Antonio; Georgiou, Chryssis; Mosteiro, Miguel A. (2016)
      The assignment and execution of tasks over the Internet is an inexpensive solution in contrast with supercomputers. We consider an Internet-based Master-Worker task computing approach, such as SETI@home. A master process ...
    • Article  

      Multi-Armed Bandits for Autonomous Test Application in RISC-V Processor Verification 

      Dimitrakopoulos, Giorgos; Kallitsounakis, E.; Takakis, Zacharias; Stefanidis, Apostolos; Nicopoulos, Chrysostomos (IEEE, 2023-07-17)
      Multi-armed bandit problems have recently received a great deal of attention, because they adequately formalize so called exploration-exploitation trade-offs arising in several relevant applications of recommendation ...
    • Conference Object  

      Multi-round Master-Worker Computing: A Repeated Game Approach 

      Fernández Anta, Antonio; Georgiou, Chryssis; Mosteiro, Miguel A.; Pareja, D. (IEEE Computer Society, 2016)
      We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers' decision of whether to comply (compute the task) or not (return a bogus ...
    • Conference Object  

      Multiagent reinforcement learning in the iterated prisoner's dilemma: Fast cooperation through evolved payoffs 

      Vassiliades, Vassilis; Christodoulou, Chris C. (2010)
      In this paper, we investigate the importance of rewards in Multiagent Reinforcement Learning in the context of the Iterated Prisoner's Dilemma. We use an evolutionary algorithm to evolve valid payoff structures with the ...
    • Article  

      Multiagent reinforcement learning with spiking and non-spiking agents in the iterated prisoner's dilemma 

      Vassiliades, Vassilis; Cleanthous, A.; Christodoulou, Chris C. (2009)
      This paper investigates Multiagent Reinforcement Learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory ...
    • Article  

      Multiagent reinforcement learning: Spiking and nonspiking agents in the Iterated Prisoner's Dilemma 

      Vassiliades, Vassilis; Cleanthous, A.; Christodoulou, Chris C. (2011)
      This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory ...
    • Article  

      Protein secondary structure prediction with bidirectional recurrent neural nets: Can weight updating for each residue enhance performance? 

      Agathocleous, Michalis; Christodoulou, Georgia; Promponas, Vasilis J.; Christodoulou, Chris C.; Vassiliades, Vassilis; Antoniou, Antonis (2010)
      Successful protein secondary structure prediction is an important step towards modelling protein 3D structure, with several practical applications. Even though in the last four decades several PSSP algorithms have been ...
    • Conference Object  

      Scalable and dynamic global power management for multicore chips 

      Otoom, M.; Trancoso, Pedro; Almasaeid, H.; Alzubaidi, M. (Association for Computing Machinery, 2015)
      The design for continuous computer performance is increasingly becoming limited by the exponential increase in the power consumption. In order to improve the energy efficiency of multicore chips, we propose a novel global ...
    • Article  

      Self-control with spiking and non-spiking neural networks playing games 

      Christodoulou, Chris C.; Banfield, G.; Cleanthous, A. (2010)
      Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that ...
    • Doctoral Thesis  Open Access

      Studies in reinforcement learning and adaptive neural networks 

      Vassiliades, Vassilis K. (Πανεπιστήμιο Κύπρου, Σχολή Θετικών και Εφαρμοσμένων Επιστημών / University of Cyprus, Faculty of Pure and Applied Sciences, 2015-08)
      Αυτή η διατριβή μελετά την προσαρμοστικότητα σε δυναμικά περιβάλλοντα (ΔΠ) και επικεντρώνεται στις περιοχές της ενισχυτικής μάθησης (ΕΜ) και των προσαρμοστικών τεχνητών νευρωνικών δικτύων (ΤΝΔ). Στα ΔΠ υπάρχει η ανάγκη για ...