Catálogo de publicaciones - libros

Logic Programs with Annotated Disjunctions and CP-logic are two different but related languages for expressing probabilistic information in logic programming. The paper presents a top down interpreter for computing the probability of a query from a program in one of these two languages. The algorithm is based on the one available for ProbLog. The performances of the algorithm are compared with those of a Bayesian reasoner and with those of the ProbLog interpreter. On programs that have a small grounding, the Bayesian reasoner is more scalable, but programs with a large grounding require the top down interpreter. The comparison with ProbLog shows that the added expressiveness effectively requires more computation resources.

We propose a layered representation of general agent models with a base layer, composed of basic agent features including control, and a higher layer, consisting of a meta-control with the task of tuning, supervising and modifying the base layer. This provides higher flexibility in how an agent is built and may evolve.

A rational agent adopts (or changes) its desires/goals when new information becomes available or its “desires” (e.g., tasks it is supposed to carry out) change. In conventional approaches on goal generation a desire is adopted if and only if conditions leading to its generation are satisfied. The fact that certain beliefs might be differently relevant in the process of desire/goal generation is not considered. As a matter of fact, a belief could be crucial for adopting a given goal but less crucial for adopting another goal. Besides, a belief could be more influent than another in the generation of a particular goal.

We propose an approach which takes into account the relevance of beliefs (more or less and more or less ) in the desire/goal generation process. More precisely, we propose a logical framework to represent changes in the mental state of an agent depending on the acquisition of new information and/or on the arising of new desires, by taking into account the fact that some beliefs may help the generation of a goal while others may prevent it.

We compare this logical framework with one where relevance of beliefs is not accounted for, and we show that the novel framework favors the adoption of a broader set of goals, exhibiting a behavior which imitates more faithfully how goals are generated/adopted in real life.

The paper addresses the problem of agents compatibility and their conformance to protocols. We assume that the specification of protocols is given in an action theory by means of temporal constraints and, in particular, communicative actions are defined in terms of their effects and preconditions on the social state of the protocol. We show that the problem of verifying the conformance of an agent with a protocol can be solved by making use of an automata based approach, and that the conformance of a set of agents with a protocol guarantees that their interaction cannot produce deadlock situations and it only gives rise to runs of the protocol.

We present two algorithms for supporting semi-automatic ontology building, integrated in a new architecturefor ontology learning from Web documents. The first algorithm automatically extracts ontological entities from tables, by using specific heuristics and WordNet-based analysis. The second algorithm harvests semantic relations from unstructured texts using Natural Language Processing techniques. The integration in allows a friendly interaction with the user for validating and modifying the extracted knowledge, and for uploading it into an existing ontology. Both algorithms show promising performance in the extraction process, and offer a practical means to speed-up the overall ontology building process.

Relational database (DB) management systems provide the standard means for structuring and querying large amounts of data. However, to access such data the exact structure of the DB must be know, and such a structure might be far from the conceptualization of a human being of the stored information. Ontologies help to bridge this gap, by providing a high level conceptual view of the information stored in a DB in a cognitively more natural way. Even in this setting, casual end users might not be familiar with the formal languages required to query ontologies. In this paper we address this issue and study the problem of ontology-based data access by means of natural language questions instead of queries expressed in some formal language. Specifically, we analyze how complex real life questions are and how far from the query languages accepted by ontology-based data access systems, how we can obtain the formal query representing a given natural language question, and how can we handle those questions which are too complex wrt the accepted query language.

This paper describes how a domain ontology has been used in practical system of query interpretation. It presents a general methodology for building a semantic and language-independent representation of the meaning of the query on the basis of the contents of the ontology. The basic idea is to look for paths on the ontology connecting concepts related to words appearing in the NL query. The final result is what has been called , i.e. a semantic description of the user’s target. Since the domain is restricted, the problem of semantic ambiguity is not as relevant as in unrestricted applications, but some hints about how to obtain unambiguous representation will be given.

Complex networks like the scale-free model proposed by Barabasi-Albert are observed in many biological systems and the application of this topology to artificial neural network leads to interesting considerations. In this paper, we present a preliminary study on how to evolve neural networks with complex topologies. This approach is utilized in the problem of modeling a chemical process with the presence of unknown inputs (disturbance). The evolutionary algorithm we use considers an initial population of individuals with differents scale-free networks in the genotype and at the end of the algorithm we observe and analyze the topology of networks with the best performances. Experimentation on modeling a complex chemical process shows that performances of networks with complex topology are similar to the feed-forward ones but the analysis of the topology of the most performing networks leads to the conclusion that the distribution of input node information affects the network performance (modeling capability).

The discovery of emerging patterns (EPs) is a descriptive data mining task defined for pre-classified data. It aims at detecting patterns which contrast two classes and has been extensively investigated for attribute-value representations. In this work we propose a method, named Mr-EP, which discovers EPs from data scattered in multiple tables of a relational database. Generated EPs can capture the differences between objects of two classes which involve properties possibly spanned in separate data tables. We implemented Mr-EP in a pre-existing multi-relational data mining system which is tightly integrated with a relational DBMS, and then we tested it on two sets of geo-referenced data.

One of the aims of modern Bioinformatics is to discover the molecular mechanisms that rule the protein operation. This would allow us to understand the complex processes involved in living systems and possibly correct dysfunctions. The first step in this direction is the identification of the functional sites of proteins.

In this paper, we propose new kernels for the automatic protein active site classification. In particular, we devise innovative attribute-value and tree substructure representations to model biological and spatial information of proteins in Support Vector Machines. We experimented with such models and the Protein Data Bank adequately pre-processed to make explicit the active site information. Our results show that structural kernels used in combination with polynomial kernels can be effectively applied to discriminate an active site from other regions of a protein. Such finding is very important since it firstly shows a successful identification of catalytic sites for a very large family of proteins belonging to a broad class of enzymes.