André Pinz Borges

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This article proposes and compares different interaction models for reinforcement learning based on multi-agent system. The cooperation during the learning process is crucial to guarantee the convergence to a good policy. The exchange of rewards among the agents during the interaction is a complex task and if it is inadequate it may cause delays in learning(More)
In this paper we propose a novel strategy for converging dynamic policies generated by adaptive agents, which receive and accumulate rewards for their actions. The goal of the proposed strategy is to speed up the convergence of such agents to a good policy in dynamic environments. Since it is difficult to have the good value for a state due to the(More)
This paper presents the development of an intelligent agent used to assist vehicle drivers. The agent has a set of resources to generate its action policy: road and vehicle features and a knowledge base containing conduct rules. The perception of the agent is ensured by a set of sensors, which provide the agent with data such as speed, position and(More)
This paper describes an intelligent approach based on agents that are able to drive and coordinate trains on stretches of railway line containing a crossing loop. Halts close to or even in crossing loops lead to increased consumption of fossil fuels, longer journey times and exhaustion of track capacity. In this paper the agents make use of a set of(More)
This paper presents a planning approach using Case-Based Reasoning (CBR) modeled as a Subsumption Architecture to generate plans for driving trains. The main idea of a planning strategy is to generate a sequence of actions for an agent, which can use these actions to change its environment. CBR allows using prior experiences for new task assignments. In the(More)
In this paper we propose an architecture of intelligent agent for automatic locomotives operating. The system agent generates its action policy using a set of resources, such as type of railway, composition, belief perception and reasoning about the actions. The focus of the operator agent is directed to the choice of acceleration points (gear) and(More)
This paper presents a methodology to obtain rules of conduction from a set of data captured from sensors placed at a train as well data of actions executed by drivers. These actions result in a history H. The knowledge discovered is put in practice in a driving simulator and the result of the simulated actions generates a history H'. The validation of the(More)