Intelligenza Artificiale - Volume 11, issue 1

Article Type: Other

Citation: Intelligenza Artificiale, vol. 11, no. 1, pp. 1-1, 2017

Authors: Maratea, Marco | Adorni, Giovanni | Cagnoni, Stefano | Gori, Marco

Article Type: Editorial

DOI: 10.3233/IA-170103

Citation: Intelligenza Artificiale, vol. 11, no. 1, pp. 3-4, 2017

Authors: Calimeri, Francesco | Fuscà, Davide | Perri, Simona | Zangari, Jessica

Article Type: Research Article

Abstract: DLV  is a powerful system for Knowledge Representation and Reasoning which supports Answer Set Programming (ASP) – a logic-based programming paradigm for solving problems in a fully declarative way. DLV  is currently widely used in academy, and, importantly, it has been fruitfully employed in many relevant industrial applications. Similarly to the other main-stream ASP systems, while processing an input program, in a first phase of the computation DLV  eliminates the variables, thus generating a ground program which is semantically equivalent to the original one, but significantly smaller than the Herbrand Instantiation, in general. This phase, called ‘grounding’, plays a key …role for the successful deployment in real-world contexts. In this work we present ℐ-DLV, a brand new version of the intelligent grounder of DLV . While relying on the solid theoretical foundations of its predecessor, it has been completely redesigned and re-engineered, both in algorithms and data structures; it now features full support to ASP-Core-2 standard language, increased flexibility and customizability, significantly improved performance, and an extensible design that eases the incorporation of language updates and optimization techniques. ℐ-DLV results in a stable and efficient ASP instantiator, that turns out to be a full-fledged deductive database system. We describe here the main features of ℐ-DLV and present the results of proper experimental activities for assessing both its applicability and performance. Show more

Keywords: Answer Set Programming, DLV, Artificial Intelligence, Knowledge Representation and Reasoning, Grounding, Instantiation, Deductive Database Systems

DOI: 10.3233/IA-170104

Citation: Intelligenza Artificiale, vol. 11, no. 1, pp. 5-20, 2017

Authors: Pozzato, Gian Luca

Article Type: Research Article

Abstract: In some application domains it is sometimes useful to reason about plausible but “not obvious” scenarios, excluding the most trivial ones. In this work we introduce nonmonotonic procedures for preferential Description Logics in order to reason about plausible but – in some sense – “surprising” scenarios. We consider an extension, called ALC + T R exp , of the nonmonotonic logic of typicality ALC + T R by inclusions of the form T  (C ) ⊑ d D , where d is a degree of expectedness. …We consider a notion of extension of an ABox, in order to assume typicality assertions about individuals satisfying cardinality restrictions on concepts, then we define a preference relation among such extended ABoxes based on the degrees of expectedness, then we restrict entailment to those extensions that are minimal with respect to this preference relation. We propose a decision procedure for reasoning in ALC + T R exp and we exploit it to show that entailment is in ExpTime as for the underlying ALC . Last, we introduce a further extension of the proposed approach in order to reason about all plausible extensions of the ABox, by restricting the attention to specific degrees of expectedness ranging from the most surprising scenarios to the most expected ones, and we show that entailment remains in ExpTime also in these cases. Show more

Keywords: Description Logics, nonmonotonic reasoning, typicality

DOI: 10.3233/IA-170105

Citation: Intelligenza Artificiale, vol. 11, no. 1, pp. 25-45, 2017

Authors: Alberti, Marco | Bellodi, Elena | Cota, Giuseppe | Riguzzi, Fabrizio | Zese, Riccardo

Article Type: Research Article

Abstract: cplint on SWISH is a web application that allows users to perform reasoning tasks on probabilistic logic programs. Both inference and learning systems can be performed: conditional probabilities with exact, rejection sampling and Metropolis-Hasting methods. Moreover, the system now allows hybrid programs, i.e., programs where some of the random variables are continuous. To perform inference on such programs likelihood weighting and particle filtering are used. cplint on SWISH is also able to sample goals’ arguments and to graph the results. This paper reports on advances and new features of cplint on SWISH, including the capability of drawing …the binary decision diagrams created during the inference processes. Show more

Keywords: probabilistic logic programming, probabilistic logical inference, hybrid program

DOI: 10.3233/IA-170106

Citation: Intelligenza Artificiale, vol. 11, no. 1, pp. 47-64, 2017

Article Type: Other

Citation: Intelligenza Artificiale, vol. 11, no. 1, pp. 65-65, 2017

Authors: Basilico, Nicola | Celli, Andrea | De Nittis, Giuseppe | Gatti, Nicola

Article Type: Research Article

Abstract: A team game is a non–cooperative normal–form game in which some teams of players play against others. Team members share a common goal but, due to some constraints, they cannot act jointly. A real–world example is the protection of environments or complex infrastructures by different security agencies: they all protect the area with valuable targets but they have to act individually since they cannot share their defending strategies (of course, they are aware of the presence of the other agents). Here, we focus on zero–sum team games with n players, where a team of n  - 1 players plays against …one single adversary. In these games, the most appropriate solution concept is the Team–maxmin equilibrium, i.e., the Nash equilibrium that ensures the team the highest payoff. We investigate the Team–maxmin equilibrium, characterizing the utility of the team and showing that it can be irrational. The problem of computing such equilibrium is NP–hard and cannot be approximated within a factor of 1 n ε . The exact solution can only be found by global optimization. We propose two approximation algorithms: the former is a modified version of an already existing algorithm, the latter is a novel anytime algorithm. We computationally investigate such algorithms, providing bounds on the utility for the team. We experimentally evaluate the algorithms analyzing their performance w.r.t. a global optimization approach and evaluate the loss due to the impossibility of correlating. Show more

Keywords: Team–maxmin equilibrium, algorithmic game theory, equilibrium computation

DOI: 10.3233/IA-170107

Citation: Intelligenza Artificiale, vol. 11, no. 1, pp. 67-79, 2017

Authors: Gavanelli, Marco

Article Type: Research Article

Abstract: Logic Programming is a programming paradigm widely used for teaching Artificial Intelligence in university courses. Prolog, probably the most popular Logic Programming language, is based on inferences similar to theorem provers, and learning it is propaedeutical for understanding formal specification languages. However, after a first phase in which students learn how specifications can be translated into executable code, a second phase is necessary for engineering the resulting program. In this second phase, having a clear picture of the execution model is crucial. In this paper, SLDNF-Draw, a program that visualizes the SLDNF operational semantics of Prolog, is presented. …SLDNF-Draw produces trees in LA TEX, the popular typesetting system, so that the generated trees have very high typographic quality and can be embedded in printed documents (e.g., course handouts) as well as in animated presentations. SLDNF Draw is completely written in Prolog, as a meta-interpreter, and does not rely on external imperative languages for the visualization. Show more

Keywords: SLDNF visualization, LATEX, Prolog, operational semantics, teaching

DOI: 10.3233/IA-170108

Citation: Intelligenza Artificiale, vol. 11, no. 1, pp. 81-92, 2017