In this paper, a restricted domain question answering (QA) system is described. The design architecture of this QA system and the features that allow the adaptation of the QA system to the medical domain are also presented. The advantages of…

This paper presents a proposal for an architecture that integrates knowledge discovery systems (automatic acquisition) and knowledge based systems (experts systems). This work formulates considerations over the viability of the implementation of this architecture according to the advance of the…

When certain enterprise domain needs to make an unusual decision, it will probably require new kind of information. If the interaction model among domains cannot be defined because the decision scenarios are unpredictable, the system itself must search the required…

The proposal of this research line is the search for alternatives to the resolution of complex problems where human knowledge should be apprehended in a general fashion. In particular, the activities developed so far can be included in the area…

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The Intellectual Creation Diffusion Service (SeDiCI) of the UNLP was created with the main objective of communicating to the society all the knowledge acquired in different academic fields of our university. This is how we can return all the efforts…

This paper describes the development of an image retrieval
system that combines probabilistic and ontological information1. The
process is divided in two different stages: indexing and retrieval. Three
information flows have been created with different kind of information
each one:…

Abstract: In today’s competitive global economy characterized by knowledge acquisition, the concept of knowledge management has become increasingly prevalent in academic and business practices. Knowledge creation is an important factor and remains a source of competitive advantage over knowledge management.…

This paper presents an adaptive learning model for market-making under the reinforcement learning framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. No knowledge of the market environment, such as the order…

This thesis considers three complications that arise from applying reinforcement learning to a real-world application. In the process of using reinforcement learning to build an adaptive electronic market-maker, we find the sparsity of data, the partial observability of the domain,…