Catálogo de publicaciones - libros

Games, by definition, offer the challenge of the presence of an opponent, to which a playing strategy should respond. In finite-timed zero-sum games, the strategy should enable to win the game within a limited playing time. Motivated by robot soccer, in this talk, we will present several approaches towards learning to select team strategies in such finite-timed zero-sum games. We will introduce an adaptive playbook approach with implicit opponent modeling, in which multiple team strategies are represented as variable weighted plays. We will discuss different plays as a function of different game situations and opponents. In conclusion, we will present an MDP-based learning algorithm to reason in particular about current score and game time left. Through extensive simulated empirical studies, we will demonstrate the effectiveness of the learning approach. In addition, the talk will include illustrative examples from robot soccer. The major part of this work is in conjunction with my PhD student Colin McMillen.

Artificial intelligence methods open up new possibilities in art and entertainment, enabling the creation of believable characters with rich personalities and emotions, interactive story systems that incorporate player interaction into the construction of dynamic plots, and interactive installations and sculptural works that are able to perceive and respond to the human environment. At the same time as AI opens up new fields of artistic expression, AI-based art itself becomes a fundamental research agenda, posing and answering novel research questions which would not be raised unless doing AI research in the context of art and entertainment. I call this agenda, in which AI research and art mutually inform each other, Expressive AI. These ideas will be illustrated by looking at several current and past projects, including the interactive drama Facade. As a new game genre, interactive drama involves socially and emotionally charged interaction with characters in the context of a dynamically evolving plot.

Corpus linguistic methods are discussed in the context of the automatic extraction of a candidate terminology of a specialist domain of knowledge. Collocation analysis of the candidate terms leads to some insight into the ontological commitment of the domain community or collective. The candidate terminology and ontology can be easily verified and validated and subsequently may be used in the construction of information extraction systems and of knowledge-based systems. The use of the methods is illustrated by an investigation of the ontological commitment of four major collectives: nuclear physics, cell biology, linguistics and anthropology. An analysis of a diachronic corpus allows an insight into changes in basic concepts within a specialism; an analysis of a corpus comprising texts published during a short and fixed time period –a synchronic corpus- shows how different sub-specialisms within a collective commit themselves to an ontology.

In this paper we deal with the problem of model-based diagnosability analysis for Web Services. The goal of diagnosability analysis is to determine whether the information one can observe during service execution is sufficient to precisely locate (by means of diagnostic reasoning) the source of the problem. The major difficulty in the context of Web Services is that models are distributed and no single entity has a global view of the complete model. In the paper we propose an approach that computes diagnosability for the decentralized diagnostic framework, described in [1], based on a Supervisor coordinating several Local Diagnosers. We also show that diagnosability analysis can be performed without requiring the Local Diagnosers different operations than those needed for diagnosis. The proposed approach is incremental: each fault is first analyzed independently of the occurrence of other faults, then the results are used to analyze combinations of behavioral modes, avoiding in most cases an exhaustive check of all combinations.

Finite model reasoning in UML class diagrams is an important task for assessing the quality of the analysis phase in the development of software applications in which it is assumed that the number of objects of the domain is finite. In this paper, we show how to encode finite model reasoning in UML class diagrams as a constraint satisfaction problem (CSP), exploiting techniques developed in description logics. In doing so we set up and solve an intermediate CSP problem to deal with the explosion of “class combinations” arising in the encoding. To solve the resulting CSP problems we rely on the use of off-the-shelf tools for constraint modeling and programming. As a result, we obtain, to the best of our knowledge, the first implemented system that performs finite model reasoning on UML class diagrams.

Temporal Logic Model Checking is a verification method having many industrial applications. This method describes a system as a formal structure called model; some properties, expressed in a temporal logic formula, can be then checked over this model. In order to improve performance, some tools allow to preprocessing the model so that a set of properties can be verified reusing the same preprocessed model. In this article, we prove that this preprocessing cannot possibly reduce complexity, if its result is bound to be of size polynomial in the size of the input. This result also holds if the formula is the part of the data that is preprocessed, which has similar practical implications.

The aim of this paper is to introduce some methodological issues about cognitive explanatory power of AI systems. We use the new concept of which is based on links between computational complexity theory and functionalism. This functionalism tries to introduce an unique intermediate, descriptive level based on the key role of heuristics. The enforcement of constraints at this level can assure a cognitive explanatory power which is not guaranteed from mere selection of modelling technique. So we reconsider the discussions about empirical underdetermination of AI systems, proposed especially for classical systems, and about the research of the “right and unique” technique for cognitive modelling. This allows us to consider the several mainstreams of cognitive artificial intelligence as different attempts to resolve underdetermination and thus, in a way, we can unify them as a manifestation of scientific pluralism.

This paper presents a new cognitive architecture for extracting meaningful, high-level information from the environment, starting from the raw data collected by a Wireless Sensor Network. The proposed framework is capable of building rich internal representation of the sensed environment by means of intelligent data processing and correlation. Furthermore, our approach aims at integrating the connectionist, data-driven model with the symbolic one, that uses a high-level knowledge about the domain to drive the environment interpretation. To this aim, the framework exploits the notion of conceptual spaces, adopting a conceptual layer between the subsymbolic one, that processes sensory data, and the symbolic one, that describes the environment by means of a high level language; this intermediate layer plays the key role of anchoring the upper layer symbols. In order to highlight the characteristics of the proposed framework, we also describe a sample application, aiming at monitoring a forest through a Wireless Sensor Network, in order to timely detect the presence of fire.

We present , a theorem prover for the KLM preferential logic of nonmonotonic reasoning. is a SICStus Prolog implementation of a , labelled tableau calculus for , obtained by introducing suitable modalities to interpret conditional assertions. The performances of are promising. can be downloaded at .

Systems competitions play a fundamental role in the advancement of the state of the art in several automated reasoning fields. The goal of such events is to answer the question: “Which system should I buy?”. In this paper, we consider voting systems as an alternative to other procedures which are well established in automated reasoning contests. Our research is aimed to compare methods that are customary in the context of social choice, with methods that are targeted to artificial settings, including a new hybrid method that we introduce.