3,896 research outputs found
CernoCAMAL : a probabilistic computational cognitive architecture
This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes.
The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally.
The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows:
- The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically.
- The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems.
- The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL.
A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis
CernoCAMAL : a probabilistic computational cognitive architecture
This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes.The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally.The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows:- The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically.- The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems.- The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL.A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis
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Seeing things as people: anthropomorphism and common-sense psychology
This thesis is about common-sense psychology and its role in cognitive science. Put simply, the argument is that common-sense psychology is important because it offers clues to some complex problems in cognitive science, and because common-sense psychology has significant effects on our intuitions, both in science and on an everyday level.
The thesis develops a theory of anthropomorphism in common-sense psychology. Anthropomorphism, the natural human tendency to ascribe human characteristics (and especially human mental characteristics) to things that aren't human, is an important theme in the thesis. Anthropomorphism reveals an endemic anthropocentricity that deeply influences our thinking about other minds. The thesis then constructs a descriptive model of anthropomorphism in common-sense psychology, and uses it to analyse two studies of the ascription of mental states. The first, Baron- Cohen et al. 's (1985) false belief test, shows how cognitive modelling can be used to compare different theories of common-sense psychology. The second study, Searle's (1980) `Chinese Room', shows 'that this same model can reproduce the patterns of scientific intuitions taken to systems which pass the Turing test (Turing, 1950), suggesting that it is best seen as a common-sense test for a mind, not a scientific one. Finally, the thesis argues that scientific theories involving the ascription of mentality through a model or a metaphor are partly dependent on each individual scientist's common-sense psychology.
To conclude, this thesis develops an interdisciplinary study of common-sense psychology and shows that its effects are more wide ranging than is commonly thought. This means that it affects science more than might be expected, but that careful study can help us to become mindful of these effects. Within this new framework, a proper understanding of common-sense psychology could lay important new foundations for the future of cognitive science
Mechanical Empathy Seems Too Risky. Will Policymakers Transcend Inertia and Choose for Robot Care? The World Needs It
An ageing population, increasing longevity and below-replacement fertility increase the care burden worldwide. This comes with age-related diseases such as Alzheimer disease and other dementias, cardiovascular disorders, cancer and—hardly noticed—pandemic loneliness. The burden, both emotionally and economically, starts to become astronomical and cannot be carried by those few who need to combine care with work and family. Social solidarity programmes are part of the answer, but they do not relieve the human helper. Yet, many hands are needed where but a few are available. Capacity issues can be solved by the introduction of care robots. Research shows that state-of-the-art technology is such that care robots can become nonthreatening social entities and be accepted and appreciated by the lonesome. Massive employment of such devices is impeded, however, sufficient governmental support of R&D is lacking—financially and regulatorily. This is where policymakers should step in and get over their moral prejudices and those of their voters and stop being afraid of losing political backing. They will regain it in the long run
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Modelling Scholarly Debate: Conceptual Foundations for Knowledge Domain Analysis Technology
Knowledge Domain Analysis (KDA) research investigates computational support for users who desire to understand and/or participate in the scholarly inquiry of a given academic knowledge domain. KDA technology supports this task by allowing users to identify important features of the knowledge domain such as the predominant research topics, the experts in the domain, and the most influential researchers. This thesis develops the conceptual foundations to integrate two identifiable strands of KDA research: Library and Information Science (LIS), which commits to a citation-based Bibliometrics paradigm, and Knowledge Engineering (KE), which adopts an ontology-based Conceptual Modelling paradigm. A key limitation of work to date is its inability to provide machine-readable models of the debate in academic knowledge domains. This thesis argues that KDA tools should support users in understanding the features of scholarly debate as a prerequisite for engaging with their chosen domain.
To this end, the thesis proposes a Scholarly Debate Ontology which specifies the formal vocabulary for constructing representations of debate in academic knowledge domains. The thesis also proposes an analytical approach that is used to automatically detect clusters of viewpoints as particularly important features of scholarly debate. This approach combines aspects of both the Conceptual Modelling and Bibliometrics paradigms. That is, the method combines an ontological focus on semantics and a graph-theoretical focus on structure in order to identify and reveal new insights about viewpoint-clusters in a given knowledge domain. This combined ontological and graph-theoretical approach is demonstrated and evaluated by modelling and analysing debates in two domains. The thesis reflects on the strengths and limitations of this approach, and considers the directions which this work opens up for future research into KDA technology
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