Reasoning with Contextual Knowledge and Influence Diagrams

Influence diagrams (IDs) are well-known formalisms, which extend Bayesian networks to model decision situations under uncertainty. Although they are convenient as a decision theoretic tool, their knowledge representation ability is limited in capturing other crucial notions such as logical consistency. In this article, we complement IDs with the light-weight description logic (DL) EL to overcome such limitations. We consider a setup where DL axioms hold in some contexts, yet the actual context is uncertain. The framework benefits from the convenience of using DL as a domain knowledge representation language and the modelling strength of IDs to deal with decisions over contexts in the presence of contextual uncertainty. We define related reasoning problems and study their computational complexity.

Network Morphology

The morphological machinery of a language is at the service of syntax, but the service can be poor. A request may result in the wrong item (deponency), or in an item the syntax already has (syncretism), or in an abundance of choices (inflectional classes or morphological allomorphy). Network Morphology regulates the service by recreating the morphosyntactic space as a network of information sharing nodes, where sharing is through inheritance, and inheritance can be overridden to allow for the regular, irregular, and, crucially, the semiregular. The network expresses the system; the way the network can be accessed expresses possible deviations from the systematic. And so Network Morphology captures the semi-systematic nature of morphology. The key data used to illustrate Network Morphology are noun inflections in the West Slavonic language Lower Sorbian, which has three genders, a rich case system and three numbers. These data allow us to observe how Network Morphology handles inflectional allomorphy, syncretism, feature neutralization, and irregularity. Latin deponent verbs are used to illustrate a Network Morphology account of morphological mismatch, where morphosyntactic features used in the syntax are expressed by morphology regularly used for different features. The analysis points to a separation of syntax and morphology in the architecture of the grammar. An account is given of Russian nominal derivation which assumes such a separation, and is based on viewing derivational morphology as lexical relatedness. Areas of the framework receiving special focus include default inheritance, global and local inheritance, default inference, and orthogonal multiple inheritance. The various accounts presented are expressed in the lexical knowledge representation language DATR, due to Roger Evans and Gerald Gazdar.

Knowledge Processing Using EKRL for Robotic Applications

This article describes a semantic framework that demonstrates an approach for modeling and reasoning based on environment knowledge representation language (EKRL) to enhance interaction between robots and their environment. Unlike EKRL, standard Binary approaches like OWL language fails to represent knowledge in an expressive way. The authors show in this work how to: model environment and interaction in an expressive way with first-order and second-order EKRL data-structures, and reason for decision-making thanks to inference capabilities based on a complex unification algorithm. This is with the understanding that robot environments are inherently subject to noise and partial observability, the authors extended EKRL framework with probabilistic reasoning based on Markov logic networks to manage uncertainty.

Conceptual Encoding and Advanced Management of Leonardo da Vinci’s Mona Lisa: Preliminary Results

This paper describes a preliminary experiment concerning the use of advanced Artificial Intelligence/Knowledge Representation techniques to improve the present formalization/digitization procedures of Cultural Heritage assets—with reference, in particular, to all types of Cultural Heritage “iconographic” entities. In this context, in agreement with the recent proposal to characterize the digital description of Cultural Heritage items making use of the notion of “Cultural Heritage Digital Twin”, we are mainly concerned with the possibility to consider not only the external, “physical”, aspects of these iconographic items but also the “message” they convey in a more or less explicit way. For our experiment, some aspects of the Mona Lisa painting by Leonardo da Vinci have been formalized, along with their context, making use of NKRL, the “Narrative Knowledge Representation Language”. NKRL is, in reality, both a Knowledge Representation language and a full Computer Science environment, used to represent/manage in an advanced way "narrative" (in the widest meaning of this word) information. The initial results of the experiment are described in the paper, along with some thoughts about their possible interest and developments.

Advanced Knowledge Representation and Reasoning Making Use of an Advanced N-ary Model

We discuss in this paper some aspects of NKRL, the Narrative Knowledge Representation Language. This is a high-level n-ary conceptual tool specially conceived for the representation and management of real world, dynamically characterized entities like situations, events and complex events, actions (e.g., in a robotics context) scripts/scenarios/narratives etc. After having pointed out some shortcomings of the standard ontological solutions for dealing with this sort of information, and having recalled some general characteristics of NKRL (like the addition of an "ontology of events" to the usual "ontology of objects"), we focus on the rules/inferential aspects proper to this language. We introduce, then, the general, formal model of "rule" used in an NKRL context and we show how this can be appropriately adapted to the setup of advanced types of inference operations based, e.g., on "analogical" and "causal" reasoning.

DatalogMTL: Computational Complexity and Expressive Power

We study the complexity and expressive power of DatalogMTL - a knowledge representation language that extends Datalog with operators from metric temporal logic (MTL) and which has found applications in ontology-based data access and stream reasoning. We establish tight PSpace data complexity bounds and also show that DatalogMTL extended with negation on input predicates can express all queries in PSpace; this implies that MTL operators add significant expressive power to Datalog. Furthermore, we provide tight combined complexity bounds for the forward-propagating fragment of DatalogMTL, which was proposed in the context of stream reasoning, and show that it is possible to express all PSpace queries in the fragment extended with the falsum predicate.

The Seventh Answer Set Programming Competition: Design and Results

Answer Set Programming (ASP) is a prominent knowledge representation language with roots in logic programming and non-monotonic reasoning. Biennial ASP competitions are organized in order to furnish challenging benchmark collections and assess the advancement of the state of the art in ASP solving. In this paper, we report on the design and results of the Seventh ASP Competition, jointly organized by the University of Calabria (Italy), the University of Genova (Italy), and the University of Potsdam (Germany), in affiliation with the 14th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2017).

Knowledge Processing Using EKRL for Robotic Applications

This article describes a semantic framework that demonstrates an approach for modeling and reasoning based on environment knowledge representation language (EKRL) to enhance interaction between robots and their environment. Unlike EKRL, standard Binary approaches like OWL language fails to represent knowledge in an expressive way. The authors show in this work how to: model environment and interaction in an expressive way with first-order and second-order EKRL data-structures, and reason for decision-making thanks to inference capabilities based on a complex unification algorithm. This is with the understanding that robot environments are inherently subject to noise and partial observability, the authors extended EKRL framework with probabilistic reasoning based on Markov logic networks to manage uncertainty.