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Applying AI for modeling and understanding analogy-based classroom teaching tools and techniques
This paper forms the final part of a short series of related articles[1,2] dedicated to highlighting a fruitful type of application of cognitively-inspired analogy engines in an educational context. It complements the earlier work with an additional fully worked out example by providing a short analysis and a detailed formal model (based on the Heuristic-Driven Theory Projection computational analogy framework) of the Number Highrise, a tool for teaching multiplication-based relations in the range of natural numbers up to 100 to children in their first years of primary school
Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing
Automatic service composition in mobile and pervasive computing faces many
challenges due to the complex and highly dynamic nature of the environment.
Common approaches consider service composition as a decision problem whose
solution is usually addressed from optimization perspectives which are not
feasible in practice due to the intractability of the problem, limited
computational resources of smart devices, service host's mobility, and time
constraints to tailor composition plans. Thus, our main contribution is the
development of a cognitively-inspired agent-based service composition model
focused on bounded rationality rather than optimality, which allows the system
to compensate for limited resources by selectively filtering out continuous
streams of data. Our approach exhibits features such as distributedness,
modularity, emergent global functionality, and robustness, which endow it with
capabilities to perform decentralized service composition by orchestrating
manifold service providers and conflicting goals from multiple users. The
evaluation of our approach shows promising results when compared against
state-of-the-art service composition models.Comment: This paper will appear on AIMS'19 (International Conference on
Artificial Intelligence and Mobile Services) on June 2
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
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Quantum theory, originally proposed as a physical theory to describe the
motions of microscopic particles, has been applied to various non-physics
domains involving human cognition and decision-making that are inherently
uncertain and exhibit certain non-classical, quantum-like characteristics.
Sentiment analysis is a typical example of such domains. In the last few years,
by leveraging the modeling power of quantum probability (a non-classical
probability stemming from quantum mechanics methodology) and deep neural
networks, a range of novel quantum-cognitively inspired models for sentiment
analysis have emerged and performed well. This survey presents a timely
overview of the latest developments in this fascinating cross-disciplinary
area. We first provide a background of quantum probability and quantum
cognition at a theoretical level, analyzing their advantages over classical
theories in modeling the cognitive aspects of sentiment analysis. Then, recent
quantum-cognitively inspired models are introduced and discussed in detail,
focusing on how they approach the key challenges of the sentiment analysis
task. Finally, we discuss the limitations of the current research and highlight
future research directions
Neuroethology, Computational
Over the past decade, a number of neural network researchers have used the term computational neuroethology to describe a specific approach to neuroethology. Neuroethology is the study of the neural mechanisms underlying the generation of behavior in animals, and hence it lies at the intersection of neuroscience (the study of nervous systems) and ethology (the study of animal behavior); for an introduction to neuroethology, see Simmons and Young (1999). The definition of computational neuroethology is very similar, but is not quite so dependent on studying animals: animals just happen to be biological autonomous agents. But there are also non-biological autonomous agents such as some types of robots, and some types of simulated embodied agents operating in virtual worlds. In this context, autonomous agents are self-governing entities capable of operating (i.e., coordinating perception and action) for extended periods of time in environments that are complex, uncertain, and dynamic. Thus, computational neuroethology can be characterised as the attempt to analyze the computational principles underlying the generation of behavior in animals and in artificial autonomous agents
The Evolution of Reaction-diffusion Controllers for Minimally Cognitive Agents
No description supplie
Between Sense and Sensibility: Declarative narrativisation of mental models as a basis and benchmark for visuo-spatial cognition and computation focussed collaborative cognitive systems
What lies between `\emph{sensing}' and `\emph{sensibility}'? In other words,
what kind of cognitive processes mediate sensing capability, and the formation
of sensible impressions ---e.g., abstractions, analogies, hypotheses and theory
formation, beliefs and their revision, argument formation--- in domain-specific
problem solving, or in regular activities of everyday living, working and
simply going around in the environment? How can knowledge and reasoning about
such capabilities, as exhibited by humans in particular problem contexts, be
used as a model and benchmark for the development of collaborative cognitive
(interaction) systems concerned with human assistance, assurance, and
empowerment?
We pose these questions in the context of a range of assistive technologies
concerned with \emph{visuo-spatial perception and cognition} tasks encompassing
aspects such as commonsense, creativity, and the application of specialist
domain knowledge and problem-solving thought processes. Assistive technologies
being considered include: (a) human activity interpretation; (b) high-level
cognitive rovotics; (c) people-centred creative design in domains such as
architecture & digital media creation, and (d) qualitative analyses geographic
information systems. Computational narratives not only provide a rich cognitive
basis, but they also serve as a benchmark of functional performance in our
development of computational cognitive assistance systems. We posit that
computational narrativisation pertaining to space, actions, and change provides
a useful model of \emph{visual} and \emph{spatio-temporal thinking} within a
wide-range of problem-solving tasks and application areas where collaborative
cognitive systems could serve an assistive and empowering function.Comment: 5 pages, research statement summarising recent publication
Computational Properties of the Hippocampus Increase the Efficiency of Goal-Directed Foraging through Hierarchical Reinforcement Learning
Sherpa Romeo green journal; open accessThe mammalian brain is thought to use a version of Model-based Reinforcement Learning (MBRL) to guide âgoal-directedâ behavior, wherein animals consider goals and make plans to acquire desired outcomes. However, conventional MBRL algorithms do not fully explain animalsâ ability to rapidly adapt to environmental changes, or learn multiple complex tasks. They also require extensive computation, suggesting that goal-directed behavior is cognitively expensive. We propose here that key features of processing in the hippocampus support a ïŹexible MBRL mechanism for spatial navigation that is computationally efïŹcient and can adapt quickly to change. We investigate this idea by implementing a computational MBRL framework that incorporates features inspired by computational properties of the hippocampus: a hierarchical representation of space, âforward sweepsâ through future spatial trajectories, and context-driven remapping of place cells. We ïŹnd that a hierarchical abstraction of space greatly reduces the computational load (mental effort) required for adaptation to changing environmental conditions, and allows efïŹcient scaling to large problems. It also allows abstract knowledge gained at high levels to guide adaptation to new obstacles. Moreover, a context-driven remapping mechanism allows learning and memory of multiple tasks. Simulating dorsal or ventral hippocampal lesions in our computational framework qualitatively reproduces behavioral deïŹcits observed in rodents with analogous lesions. The framework may thus embody key features of how the brain organizes model-based RL to efïŹciently solve navigation and other difïŹcult tasks.Ye
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