17,453 research outputs found
The Birth of Pictoriality in Computer Media
The aim of the paper is to follow some milestones of the story of
computer media as far as the notion of pictoriality is concerned. I am
going to describe in the most general way how it happens that two quite
separate technologies as computer machine and pictorial representation
met and since then became almost inseparable
Playing Smart - Artificial Intelligence in Computer Games
Abstract: With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games
Probabilistic Methodology and Techniques for Artefact Conception and Development
The purpose of this paper is to make a state of the art on probabilistic methodology and techniques for artefact conception and development. It is the 8th deliverable of the BIBA (Bayesian Inspired Brain and Artefacts) project. We first present the incompletness problem as the central difficulty that both living creatures and artefacts have to face: how can they perceive, infer, decide and act efficiently with incomplete and uncertain knowledge?. We then introduce a generic probabilistic formalism called Bayesian Programming. This formalism is then used to review the main probabilistic methodology
and techniques. This review is organized in 3 parts: first the probabilistic models from Bayesian networks to Kalman filters and from sensor fusion to CAD systems, second the inference techniques and finally the learning and model acquisition and comparison methodologies. We conclude with the perspectives of the BIBA project as they rise from this state of the art
Memory Structure and Cognitive Maps
A common way to understand memory structures in the cognitive sciences is as a cognitive map.
Cognitive maps are representational systems organized by dimensions shared with physical space. The
appeal to these maps begins literally: as an account of how spatial information is represented and used
to inform spatial navigation. Invocations of cognitive maps, however, are often more ambitious;
cognitive maps are meant to scale up and provide the basis for our more sophisticated memory
capacities. The extension is not meant to be metaphorical, but the way in which these richer mental
structures are supposed to remain map-like is rarely made explicit. Here we investigate this missing
link, asking: how do cognitive maps represent non-spatial information? We begin with a survey of
foundational work on spatial cognitive maps and then provide a comparative review of alternative,
non-spatial representational structures. We then turn to several cutting-edge projects that are engaged
in the task of scaling up cognitive maps so as to accommodate non-spatial information: first, on the
spatial-isometric approach , encoding content that is non-spatial but in some sense isomorphic to
spatial content; second, on the abstraction approach , encoding content that is an abstraction over
first-order spatial information; and third, on the embedding approach , embedding non-spatial
information within a spatial context, a prominent example being the Method-of-Loci. Putting these
cases alongside one another reveals the variety of options available for building cognitive maps, and the
distinctive limitations of each. We conclude by reflecting on where these results take us in terms of
understanding the place of cognitive maps in memory
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
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