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ISSN 1724-8035 (P)
ISSN 2211-0097 (E)
Intelligenza Artificiale is the official journal of the Italian Association for Artificial Intelligence (AI*IA). Intelligenza Artificiale publishes rigorously reviewed articles (in English) in all areas of Artificial Intelligence, with a special attention to original contributions. It will also publish assessments of the state of the art in various areas of AI, and innovative system descriptions with appropriate evaluation.
The Editor-in-Chief welcomes proposals for special issues, book reviews, conference reports and news items of interest to the AI research community. Intelligenza Artificiale is an international journal and welcomes submissions from every country.
Abstract: Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores the possibility of using generative learning techniques for automatic assessment of creativity. The proposed solution does not involve human judgement, it is modular and of general applicability. We introduce a new measure, namely DeepCreativity , based on Margaret Boden’s definition of creativity as composed by value , novelty and surprise . We evaluate our methodology (and related measure) considering a case study, i.e., the generation of 19th century American poetry, showing its effectiveness and expressiveness.
Keywords: Computational creativity, deep learning, creativity measure, American
Abstract: In this paper, we provide a unified presentation of the Configurable Markov Decision Process (Conf-MDP) framework. A Conf-MDP is an extension of the traditional Markov Decision Process (MDP) that models the possibility to configure some environmental parameters. This configuration activity can be carried out by the learning agent itself or by an external configurator. We introduce a general definition of Conf-MDP, then we particularize it for the cooperative setting, where the configuration is fully functional to the agent’s goals, and non-cooperative setting, in which agent and configurator might have different interests. For both settings, we propose suitable solution concepts.…Furthermore, we illustrate how to extend the traditional value functions for MDPs and Bellman operators to this new framework.
Keywords: Reinforcement learning, Markov
Decision Process, configurable Markov Decision Process
Abstract: Preferences are ubiquitous in our everyday life. They are essential in the decision making process of individuals. Recently, they have also been employed to represent ethical principles, normative systems or guidelines. In this work we focus on a ceteris paribus semantics for deontic logic: a state of affairs where a larger set of respected prescriptions is preferable to a state of affairs where some are violated. Conditional preference networks (CP-nets) are a compact formalism to express and analyse ceteris paribus preferences, with some desirable computational properties. In this paper, we show how deontic concepts (such as contrary-to-duty obligations) can be…modeled with generalized CP-nets (GCP-nets) and how to capture the distinction between strong and weak permission in this formalism. To do that, we leverage on an existing restricted deontic logic that will be mapped into conditional preference nets.