AGM-Like Paraconsistent Belief Change

Two systems of belief change based on paraconsistent logics are introduced in this article by means of AGM-like postulates. The first one, AGMp, is defined over any paraconsistent logic which extends classical logic such that the law of excluded middle holds w.r.t. the paraconsistent negation. The second one, AGMo , is specifically designed for paraconsistent logics known as Logics of Formal Inconsistency (LFIs), which have a formal consistency operator that allows to recover all the classical inferences. Besides the three usual operations over belief sets, namely expansion, contraction and revision (which is obtained from contraction by the Levi identity), the underlying paraconsistent logic allows us to define additional operations involving (non-explosive) contradictions. Thus, it is defined external revision (which is obtained from contraction by the reverse Levi identity), consolidation and semi-revision, all of them over belief sets. It is worth noting that the latter operations, introduced by S. Hansson, involve the temporary acceptance of contradictory beliefs, and so they were originally defined only for belief bases. Unlike to previous proposals in the literature, only defined for specific paraconsistent logics, the present approach can be applied to a general class of paraconsistent logics which are supraclassical, thus preserving the spirit of AGM. Moreover, representation theorems w.r.t. constructions based on selection functions are obtained for all the operations

End-to-end multi-level dialog act recognition

The three-level dialog act annotation scheme of the DIHANA corpus poses a multi-level classification problem in which the bottom levels allow multiple or no labels for a single segment. We approach automatic dialog act recognition on the three levels using an end-to-end approach, in order to implicitly capture relations between them. Our deep neural network classifier uses a combination of word- and character-based segment representation approaches, together with a summary of the dialog history and information concerning speaker changes. We show that it is important to specialize the generic segment representation in order to capture the most relevant information for each level. On the other hand, the summary of the dialog history should combine information from the three levels to capture dependencies between them. Furthermore, the labels generated for each level help in the prediction of those of the lower levels. Overall, we achieve results which surpass those of our previous approach using the hierarchical combination of three independent per-level classifiers. Furthermore, the results even surpass the results achieved on the simplified version of the problem approached by previous studies, which neglected the multi-label nature of the bottom levels and only considered the label combinations present in the corpus.info:eu-repo/semantics/publishedVersio

A curiosity model for artificial agents

Curiosity is an inherent characteristic of the animal instinct, which stimulates the need to obtain further knowledge and leads to the exploration of the surrounding environment. In this document we present a computational curiosity model, which aims at simulating that kind of behavior on artificial agents. This model is influenced by the two main curiosity theories defended by psychologists – Curiosity Drive Theory and Optimal Arousal Model. By merging both theories, as well as aspects from other sources, we concluded that curiosity can be defined in terms of the agent’s personality, its level of arousal, and the interest of the object of curiosity. The interest factor is defined in terms of the importance of the object of curiosity to the agent’s goals, its novelty, and surprise. To assess the performance of the model in practice, we designed a scenario consisting of virtual agents exploring a tile-based world, where objects may exist. The performance of the model in this scenario was evaluated in incremental steps, each one introducing a new component to the model. Furthermore, in addition to empirical evaluation, the model was also subjected to evaluation by human observers. The results obtained from both sources show that our model is able to simulate curiosity on virtual agents and that each of the identified factors has its role in the simulation.info:eu-repo/semantics/acceptedVersio

Automatic recognition of the general-purpose communicative functions defined by the ISO 24617-2 standard for dialog act annotation (Extended abstract)

From the perspective of a dialog system, the identification of the intention behind the segments in a dialog is important, as it provides cues regarding the information present in the segments and how they should be interpreted. The ISO 24617-2 standard for dialog act annotation defines a hierarchically organized set of general-purpose communicative functions that correspond to different intentions that are relevant in the context of a dialog. In this paper, we explore the automatic recognition of these functions. To do so, we propose to adapt existing approaches to dialog act recognition, so that they can deal with the hierarchical classification problem. More specifically, we propose the use of an end-to-end hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Additionally, we rely on transfer learning processes to address the data scarcity problem. Our experiments on the DialogBank show that this approach outperforms both flat and hierarchical approaches based on multiple classifiers and that each of its components plays an important role in the recognition of general-purpose communicative functionsinfo:eu-repo/semantics/publishedVersio