Kyriakos C. Chatzidimitriou

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The leap from decision support to autonomous systems has often raised a number of issues, namely system safety, soundness and security. Depending on the field of application, these issues can either be easily overcome or even hinder progress. In the case of Supply Chain Management (SCM), where system performance implies loss or profit, these issues are of(More)
Supply Chain Management (SCM) has recently entered a new era, where the old-fashioned static, long-term relationships between involved actors are being replaced by new, dynamic negotiating schemas, established over virtual organizations and trading marketplaces. SCM environments now operate under strict policies that all interested parties (suppliers,(More)
The development of real-world, fully autonomous agents would require mechanisms that would offer generalization capabilities from experience, suitable for a large range of machine learning tasks, like those from the areas of supervised and reinforcement learning. Such capacities could be offered by parametric function approximators that could either model(More)
As multi-agent systems and information agents obtain an increasing acceptance by application developers, existing legacy Enterprise Resource Planning (ERP) systems still provide the main source of data used in customer, supplier and inventory resource management. In this paper we present a multi-agent system, comprised of information agents, which(More)
The major goal of transfer learning is to transfer knowledge acquired on a source task in order to facilitate learning on another, different, but usually related, target task. In this paper, we are using neuroevolution to evolve echo state networks on the source task and transfer the best performing reservoirs to be used as initial population on the target(More)
In modern supply chains, stakeholders with varying degrees of autonomy and intelligence compete against each other in a constant effort to establish beneficiary contracts and maximize their own revenue. In such competitive environments, entities-software agents being a typical programming paradigm-interact in a dynamic and versatile manner, so each action(More)
The task-oriented nature of data mining (DM) has already been dealt successfully with the employment of intelligent agent systems that distribute tasks, collaborate and synchronize in order to reach their ultimate goal, the extraction of knowledge. A number of sophisticated multi-agent systems (MAS) that perform DM have been developed, proving that agent(More)
Real Time Strategy games (RTS) provide an interesting test bed for agents that use Reinforcement Learning (RL) algorithms. From an agent's point of view, RTS games constitute a Markovian, partially observable and dynamic environment with a huge state space. In this paper, we present an agent that uses a Zeroth-level Classifier System (ZCS) in order to(More)
Predicting the future behavior of tropical cyclones is a problem of great importance for the atmospheric science community with concrete applications. Researchers understand enough about modeling storm systems to predict their track, but forecasting their future intensity remains elusive. In the present study, we formulate tropical storm intensification(More)