821 research outputs found
Ontologies, Mental Disorders and Prototypes
As it emerged from philosophical analyses and cognitive research, most concepts exhibit typicality effects, and resist to the efforts of defining them in terms of necessary and sufficient conditions. This holds also in the case of many medical concepts. This is a problem for the design of computer science ontologies, since knowledge representation formalisms commonly adopted in this field do not allow for the representation of concepts in terms of typical traits. However, the need of representing concepts in terms of typical traits concerns almost every domain of real world knowledge, including medical domains. In particular, in this article we take into account the domain of mental disorders, starting from the DSM-5 descriptions of some specific mental disorders. On this respect, we favor a hybrid approach to the representation of psychiatric concepts, in which ontology oriented formalisms are combined to a geometric representation of knowledge based on conceptual spaces
Heterogeneous Proxytypes Extended: Integrating Theory-like Representations and Mechanisms with Prototypes and Exemplars
The paper introduces an extension of the proposal according to which
conceptual representations in cognitive agents should be intended as heterogeneous
proxytypes. The main contribution of this paper is in that it details how
to reconcile, under a heterogeneous representational perspective, different theories
of typicality about conceptual representation and reasoning. In particular, it
provides a novel theoretical hypothesis - as well as a novel categorization algorithm
called DELTA - showing how to integrate the representational and reasoning
assumptions of the theory-theory of concepts with the those ascribed to the
prototype and exemplars-based theories
The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments
In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge.
We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and
future challenges
Ontologies, Disorders and Prototypes
As it emerged from philosophical analyses and cognitive research, most concepts exhibit typicality effects, and resist to the efforts of defining them in terms of necessary and sufficient conditions. This holds also in the case of many medical concepts. This is a problem for the design of computer science ontologies, since knowledge representation formalisms commonly adopted in this field (such as, in the first place, the Web Ontology Language - OWL) do not allow for the representation of concepts in terms of typical traits. The need of representing concepts in terms of typical traits concerns almost every domain of real world knowledge, including medical domains. In particular, in this article we take into account the domain of mental disorders, starting from the DSM-5 descriptions of some specific disorders. We favour a hybrid approach to concept representation, in which ontology oriented formalisms are combined to a geometric representation of knowledge based on conceptual space. As a preliminary step to apply our proposal to mental disorder concepts, we started to develop an OWL ontology of the schizophrenia spectrum, which is as close as possible to the DSM-5 descriptions
Non classical concept representation and reasoning in formal ontologies
Formal ontologies are nowadays widely considered a standard tool for knowledge
representation and reasoning in the Semantic Web. In this context, they are expected to
play an important role in helping automated processes to access information. Namely:
they are expected to provide a formal structure able to explicate the relationships
between different concepts/terms, thus allowing intelligent agents to interpret, correctly,
the semantics of the web resources improving the performances of the search
technologies.
Here we take into account a problem regarding Knowledge Representation in general,
and ontology based representations in particular; namely: the fact that knowledge
modeling seems to be constrained between conflicting requirements, such as
compositionality, on the one hand and the need to represent prototypical information on
the other. In particular, most common sense concepts seem not to be captured by the
stringent semantics expressed by such formalisms as, for example, Description Logics
(which are the formalisms on which the ontology languages have been built). The aim
of this work is to analyse this problem, suggesting a possible solution suitable for
formal ontologies and semantic web representations.
The questions guiding this research, in fact, have been: is it possible to provide a formal
representational framework which, for the same concept, combines both the classical
modelling view (accounting for compositional information) and defeasible, prototypical
knowledge ? Is it possible to propose a modelling architecture able to provide different
type of reasoning (e.g. classical deductive reasoning for the compositional component
and a non monotonic reasoning for the prototypical one)?
We suggest a possible answer to these questions proposing a modelling framework able
to represent, within the semantic web languages, a multilevel representation of
conceptual information, integrating both classical and non classical (typicality based)
information. Within this framework we hypothesise, at least in principle, the coexistence of multiple reasoning processes involving the different levels of
representation
Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems
In this paper I will present an analysis of the impact that the notion of “bounded rationality”,
introduced by Herbert Simon in his book “Administrative Behavior”, produced in the
field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated
Decision Making (ADM), I will show how the introduction of the cognitive dimension into
the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the
development of a line of research aiming at the realisation of artificial systems whose decisions
are based on the adoption of powerful shortcut strategies (known as heuristics) based
on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the
“heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial
complexity in real-life situations and still represents an important strategy for the design
and implementation of intelligent systems
Dual PECCS: A Cognitive System for Conceptual Representation and Categorization
In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and –in addition– that it may be plausibly applied to different general computational models of cognition. The current version of the system, in fact, extends our previous work, in that Dual-PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation
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