Search

We introduce a family of operators to combine Description Logic concepts. They aim to characterise complex concepts that apply to instances that satisfy "enough" of the concept descriptions given. For instance, an individual might not have any tusks, but still be considered an elephant. To formalise the meaning of "enough", the operators take a list of weighted concepts as arguments, and a certain threshold to be met. We commence a study of the formal properties of these operators, and study some variations. The intended applications concern the representation of cognitive aspects of classification tasks: the interdependencies among the attributes that define a concept, the prototype of a concept, and the typicality of the instances.

We discuss steps towards a formalisation of the principles of an agentive naïve proto-physics, designed to match a level of abstraction that reflects the pre-linguistic conceptualisations and elementary notions of agency, as they develop during early human cognitive development. To this end, we present an agentive extension of the multi-dimensional image schema logic ISL based on variants of STIT theory, thus replacing the temporal dimension of ISL with an action-agnostic theory of agency. To begin grasping the notion of 'animate agent', we apply the newly defined logic to model the image schematic notion of 'self movement' as a means to distinguish the agentive capabilities of a mouse from those of a ball. Finally, we outline the prospects for employing the theory in cognitive robotics.

When considering two concepts in terms of extensional logic, their combination will often be trivial, returning an empty extension. Consider e.g. “a Fish Vehicle”, i.e., “a Vehicle which is also a Fish”. Still, people use sophisticated strategies to produce new, non-empty concepts. All these strategies involve the human ability to mend the conflicting attributes of the input concepts and to create new properties of the combination. We focus in particular on the case where a Head concept has superior ‘asymmetric’ control over steering the resulting combination (or hybridisation) with a Modifier concept. Specifically, we propose a dialogical model of the cognitive and logical mechanics of this asymmetric form of hybridisation. Its implementation is then evaluated using a combination of example ontologies.

The aim of this abstract is to introduce a formal framework enabling to reason about resource-sensitive uses of artifacts. To achieve this, we integrate (non-normal) modalities into Intuitionistic Linear Logic. The function of an artifact is a (resource-sensitive) linear implication and we interpret each modality as an agent's bringing about of resources.

Axiom weakening is a novel technique that allows for fine-grained repair of inconsistent ontologies. In a multi-agent setting, integrating ontologies corresponding to multiple agents may lead to inconsistencies. Such inconsistencies can be resolved after the integrated ontology has been built, or their generation can be prevented during ontology generation. We implement and compare these two approaches. First, we study how to repair an inconsistent ontology resulting from a voting-based aggregation of views of heterogeneous agents. Second, we prevent the generation of inconsistencies by letting the agents engage in a turn-based rational protocol about the axioms to be added to the integrated ontology. We instantiate the two approaches using real-world ontologies and compare them by measuring the levels of satisfaction of the agents w.r.t. the ontology obtained by the two procedures.

Ontologies represent principled, formalised descriptions of agents’ conceptualisations of a domain. For a community of agents, these descriptions may significantly differ. We propose an aggregative view of the integration of ontologies based on Judgement Aggregation (JA). Agents may vote on statements of the ontologies, and we aim at constructing a collective, integrated ontology, that reflects the individual conceptualisations as much as possible. As several results in JA show, many attractive and widely used aggregation procedures are prone to return inconsistent collective ontologies. We propose to solve the possible inconsistencies in the collective ontology by applying suitable weakenings of axioms that cause inconsistencies.

We address the problem of analysing the joint coherence of a number of concepts with respect to a background ontology. To address this problem, we explore the applicability of Paul Thagard's computational theory of coherence, in combination with semantic similarity between concepts based on a generalisation operator. In particular, given the input concepts, our approach computes maximally coherent subsets of these concepts following Thagard's partitioning approach, whilst returning a number of possible generalisations of these concepts as justifi- cation of why these concepts cohere.

We describe two applications of refinement operators that can generalise and specialise concepts expressed in the ALC description logic language. The first application addresses the problem of analysing the joint coherence of some given concepts w.r.t. a background ontology. To this end, we apply Thagard's computational theory of coherence, in combination with semantic similarity between concepts defined by means of the generalisation operator. The second application focuses on repairing an inconsistent collective ontology that may result from the vote on axioms of multiple experts based on principles from social choice theory and judgment aggregation. We use the refinement operators, together with a reference ontology, to weaken some axioms and to repair the collective ontology.