Cláudio Adriano Policastro

Node-Depth Encoding for Evolutionary Algorithms Applied to Network Design

Network design involves several areas of engineering and science. Computer networks, electrical circuits, transportation problems, and phylogenetic trees are some examples. In general, these problems are NP-Hard. In order to deal with the complexity of these problems, some alternative strategies have been proposed. Approaches using evolutionary algorithms have achieved relevant results. However, the graph encoding is critical for the performance of such approaches in network design problems. Aiming to overcome this drawback, alternative representations of spanning trees have been developed. This article proposes an encoding for generation of spanning forests by evolutionary algorithms. The proposal is evaluated for degree-constrained minimum spanning tree problem.

A hybrid case adaptation approach for case-based reasoning

Abstract Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.

Hybrid Approaches for Case Retrieval and Adaptation

The number of researches on hybrid models has been grown significantly in the last years, both in the development of intelligent systems and in the study of cognitive models. The integration of Case Based Reasoning and Artificial Neural Networks has received large attention by the area of neurosymbolic models. This paper proposes a new Case Based Reasoning approach using hybrid mechanisms for case retrieval and adaptation.

Robotic Architecture Inspired on Behavior Analysis

Social robots are embodied agents that are part of a heterogeneous group: a society of robots or humans. They are able to recognize human beings and each other, and engage in social interactions. They possess histories and they explicitly communicate and learn from interactions. The construction of social robots may strongly benefit from using a robotic architecture. However, a robotic architecture for sociable robots must have structures and mechanism to allow social interaction control and learning from environment. In this paper, we propose a robotic architecture inspired on Behavior Analysis. Methods and structures of the proposed architecture are presented and discussed. The architecture was evaluated on a Skinner Box simulator and the obtained results shown that the architecture is able to produce appropriate behavior and to learn from social interaction.

Learning of shared attention in sociable robotics

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They should be able to recognize human beings and each other, and to engage in social interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction, behavior control and learning from environment. Learning processes described on Science of Behavior Analysis may lead to the development of promising methods and structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation, are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction.

Automatic knowledge learning and case adaptation with a hybrid committee approach

Abstract When Case Based Reasoning systems are applied to real-world problems, the retrieved solutions usually require adaptations in order to be used on new contexts. Therefore, case adaptation is a desirable capability. However, case adaptation is still a challenge for this research area. In general, the acquisition of knowledge for case adaptation is harder than the acquisition of cases. This paper explores the automatic learning of adaptation knowledge and explores the use of a hybrid committee approach for automatic case adaptation.

A hybrid case based reasoning approach for monitoring water quality

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

Relational reinforcement learning applied to shared attention

This paper describes the design and implementation of a learning method in the context of robotic architecture for the social interactive simulation. This method is based on TG algorithm, named ETG, but use incremental process during the episode of learning. So, it does not use secondary memory to storage examples before insert in relational regression engine. This make easier the agent to choose the action with a greater degree of accuracy. The performance of ETG has been tested into a robotic architecture that control a head robotic. Then, a set of empirical evaluations has been conducted in the social interactive simulator for performing the task of shared attention. The experimental results show that the proposed algorithm is able to produce appropriate learning capability for shared attention.

An enhancement of relational reinforcement learning

Relational reinforcement learning is a technique that combines reinforcement learning with relational learning or inductive logic programming. This technique offers greater expressive power than that one offered by traditional reinforcement learning. However, there are some problems when one wish to use it in a real time system. Most of recent research interests on incremental relational learning structure, that is a great challenge in this area. In this work, we are proposing an enhancement of TG algorithm and we illustrate the approach with a preliminary experiment. The algorithm was evaluated on a Blocks World simulator and the obtained results shown it is able to produce appropriate learn capability.

Hybrid knowledge representation applied to the learning of the shared attention

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They are able to recognize human beings and each other, and engage in social interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. However, a robotic architecture for sociable robots must have structures and mechanisms to allow social interaction, behavior control and learning from environment. In this article, a new hybrid knowledge representation is proposed and integrated to our robotic architecture inspired on Behavior Analysis. This new hybrid knowledge representation enables incremental learning and knowledge generalization by incorporating an ART2 neural network combined with a relational presentation of first order. The new representation has been evaluated in the context of the learning of the shared attention and the results obtained show that it is a very promising approach.