Space, State
The Mickey Leland Papers & Collection Addendum. (Texas Southern University, 2015) · Digital Scholarship - Texas Southern University (Texas Southern University) · 1987
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About 10,000 papers for “State space”
State Space
Heiko J. Herrmann · 2008
The aim of this article is to show, how computer algebra can be used when applying Liu’s procedure. Although Mathematica r ○ (a commercial product by Wolfram Research Inc.) is used in this article, it is possible to use other computer algebra systems as well. 1
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State-space
Steven L. Brunton · 2016
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State space
David Terman, Eugene M. Izhikevich · Scholarpedia · 2008
Cited by 7Open access
State Space
2000
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state–space
Fabien Campillo, Rivo Rakotozafy · 2012
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State Space
Peter Joseph Rayer · 2020
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Space, State
2015) The Mickey Leland Papers & Collection Addendum. (Texas Southern University · Digital Scholarship - Texas Southern University (Texas Southern University) · 1987
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State Space Modeling
Filomena Maggino · 2023
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State Space Models
Andrew Harvey · The New Palgrave Dictionary of Economics · 2008
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Planning state-space.
Marta Kryven (20176022), Suhyoun Yu (20176025), Max Kleiman-Weiner (5966036) et al. · Figshare · 2024
<div><p>How people plan is an active area of research in cognitive science, neuroscience, and artificial intelligence. However, tasks traditionally used to study planning in the laboratory tend to be constrained to artificial environments, such as Chess and bandit problems. To date there is still no agreed-on model of how people plan in realistic contexts, such as navigation and search, where values intuitively derive from interactions between perception and cognition. To address this gap and move towards a more naturalistic study of planning, we present a novel spatial Maze Search Task (MST) where the costs and rewards are physically situated as distances and locations. We used this task in two behavioral experiments to evaluate and contrast multiple distinct computational models of planning, including optimal expected utility planning, several one-step heuristics inspired by studies of information search, and a family of planners that deviate from optimal planning, in which action values are estimated by the interactions between perception and cognition. We found that people’s deviations from optimal expected utility are best explained by planners with a limited horizon, however our results do not exclude the possibility that in human planning action values may be also affected by cognitive mechanisms of numerosity and probability perception. This result makes a novel theoretical contribution in showing that limited planning horizon generalizes to spatial planning, and demonstrates the value of our multi-model approach for understanding cognition.</p></div>
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State-Space Models
Steve Pressé, Ioannis Sgouralis · Cambridge University Press eBooks · 2023
In this chapter we extend our discussion of the previous chapter to model dynamical systems with continuous state-spaces. We present statistical formulations to model and analyze noisy trajectories that evolve in a continuous state space whose output is corrupted by noise. In particular, we place special emphasis on linear Gaussian state-space models and, within this context, present Kalman filtering theory. The theory presented herein lends itself to the exploration of tracking algorithms explored in the chapter and in an end-of-chapter project.
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State-Space Models
Ken B. Newman, S. T. Buckland, Byron J. T. Morgan et al. · Methods in statistical ecology · 2014
Cited by 6
State Space Models
Terence C. Mills · Elsevier eBooks · 2019
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State Space Grids
Tom Hollenstein · 2012
Cited by 110
State Space Modelling
Thierry Miquel · HAL (Le Centre pour la Communication Scientifique Directe) · 2020
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State Space Models
Joshua C. C. Chan, Dirk P. Kroese · Springer texts in statistics · 2025
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State space models
OSINGA, Ernst C. · Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University) · 2017
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State-Space Constraints
Marc Peter Deisenroth, Carl Edward Rasmussen, Dieter Fox · 2011
Low-cost robot arm by Lynxmotion. Affordable, robust manipulation in unstructured environments Key problems: inaccurate, noisy, limited sensor feedback Automatically learn to solve tasks using reinforcement learning Problem: RL without strong prior knowledge requires too much training (low-cost robots quickly wear out) Here: Use available data very efficiently, principled treatment of uncertainty Here: Demonstration on a block-stacking task with an off-the-shelf system
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State-Space Models
James D. Hamilton · RePEc: Research Papers in Economics
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