Abstract. This paper deals with the issue of learning in multi-agent systems (MAS). Particularly, we are interested in BDI (Belief, Desire, Intention) agents. Despite the relevance of the BDI model of rational agency, little work has been done to deal with its two main limitations: i) The lack of learning competences; and ii) The lack of explicit multiagent functionality. From the multi-agent learning perspective, we propose a BDI agent architecture extended with learning competences for MAS context. Induction of Logical Decision Trees, a first order method, is used to enable agents to learn when their plans are successfully executable. Our implementation enables multiple agents executed as parallel functions in a single Lisp image. In addition, our approach maintains consistency between learning and the theory of practical reasoning.

Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool.

Appropriate teaching–learning strategies lead to student engagement during learning activities. Scientific progress and modern technology have made it possible to measure engagement in educational settings by reading and analyzing student physiological signals through sensors attached to wearables. This work is a review of current student engagement detection initiatives in the educational domain. The review highlights existing commercial and non-commercial wearables for student engagement monitoring and identifies key physiological signals involved in engagement detection. Our findings reveal that common physiological signals used to measure student engagement include heart rate, skin temperature, respiratory rate, oxygen saturation, blood pressure, and electrocardiogram (ECG) data. Similarly, stress and surprise are key features of student engagement.

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National audienceThis paper presents JILDT (Jason Induction of Logical Decision Trees), a library that defines two learning agent classes for Jason, the well known java-based implementation of AgentSpeak(L). Agents defined as instances of JILDT can learn about their reasons to adopt intentions performing first-order induction of decision trees. A set of plans and actions are defined in the library for collecting training examples of executed intentions, labeling them as succeeded or failed executions, computing the target language for the induction, and using the induced trees to modify accordingly the plans of the learning agents. The library is tested studying commitment: A simple problem in a world of blocks is used to compare the behavior of a default Jason agent that does not reconsider his intentions, unless they fail; a learning agent that reconsiders when to adopt intentions by experience; and a single-minded agent that also drops intentions when this is rational. Results are very promissory for both, justifying a formal theory of single-mind commitment based on learning, as well as enhancing the adopted inductive process

The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a complex model that generalizes poorly). Unfortunately, there are many situations where we simply do not have this required amount of data. Thus, we need to find methods capable of efficiently exploiting the available data while avoiding overfitting. Different metrics have been proposed to achieve this goal: the Minimum Description Length principle (MDL), Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC), among others. In this paper, we focus on crude MDL and empirically evaluate its performance in selecting models with a good balance between goodness of fit and complexity: the so-called bias-variance dilemma, decomposition or tradeoff. Although the graphical interaction between these dimensions (bias and variance) is ubiquitous in the Machine Learning literature, few works present experimental evidence to recover such interaction. In our experiments, we argue that the resulting graphs allow us to gain insights that are difficult to unveil otherwise: that crude MDL naturally selects balanced models in terms of bias-variance, which not necessarily need be the gold-standard ones. We carry out these experiments using a specific model: a Bayesian network. In spite of these motivating results, we also should not overlook three other components that may significantly affect the final model selection: the search procedure, the noise rate and the sample size.

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