292,982 research outputs found
Agent-based models and individualism: is the world agent-based?
Agent-based models (ABMs) are an increasingly popular tool in the social sciences. This trend seems likely to continue, so that they will become widely used in geography and in urban and regional planning. We present an overview of examples of these models in the life sciences, economics, planning, sociology, and archaeology. We conclude that ABMs strongly tend towards an individualist view of the social world. This point is reinforced by closer consideration of particular examples. This discussion pays attention to the inadequacy of an individualist model of society with reference to debates in social theory. We argue that because models are closed representations of an open world it is important that institutions and other social structures be explicitly included, or that their omission be explained. A tentative explanation for the bias of ABMs is offered, based on an examination of early research in artificial intelligence and distributed artificial intelligence from which disciplines the approach is derived. Some implications of these findings are discussed. We indicate some useful research directions which are beginning to tackle the individualism issue directly. We further note that the underlying assumptions of ABMs are often hidden in the implementation details. We conclude that such models must be subject to critical examination of their assumptions, and that model builders should engage with social theory if the approach is to realise its full potential
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
Research in Artificial Intelligence (AI) has focused mostly on two extremes:
either on small improvements in narrow AI domains, or on universal theoretical
frameworks which are usually uncomputable, incompatible with theories of
biological intelligence, or lack practical implementations. The goal of this
work is to combine the main advantages of the two: to follow a big picture
view, while providing a particular theory and its implementation. In contrast
with purely theoretical approaches, the resulting architecture should be usable
in realistic settings, but also form the core of a framework containing all the
basic mechanisms, into which it should be easier to integrate additional
required functionality.
In this paper, we present a novel, purposely simple, and interpretable
hierarchical architecture which combines multiple different mechanisms into one
system: unsupervised learning of a model of the world, learning the influence
of one's own actions on the world, model-based reinforcement learning,
hierarchical planning and plan execution, and symbolic/sub-symbolic integration
in general. The learned model is stored in the form of hierarchical
representations with the following properties: 1) they are increasingly more
abstract, but can retain details when needed, and 2) they are easy to
manipulate in their local and symbolic-like form, thus also allowing one to
observe the learning process at each level of abstraction. On all levels of the
system, the representation of the data can be interpreted in both a symbolic
and a sub-symbolic manner. This enables the architecture to learn efficiently
using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
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