15,583 research outputs found
Rational physical agent reasoning beyond logic
The paper addresses the problem of defining a theoretical physical agent framework that satisfies practical requirements of programmability by non-programmer engineers and at the same time permitting fast realtime operation of agents on digital computer networks. The objective of the new framework is to enable the satisfaction of performance requirements on autonomous vehicles and robots in space exploration, deep underwater exploration, defense reconnaissance, automated manufacturing and household automation
Thresholds in layered neural networks with variable activity
The inclusion of a threshold in the dynamics of layered neural networks with
variable activity is studied at arbitrary temperature. In particular, the
effects on the retrieval quality of a self-controlled threshold obtained by
forcing the neural activity to stay equal to the activity of the stored paterns
during the whole retrieval process, are compared with those of a threshold
chosen externally for every loading and every temperature through optimisation
of the mutual information content of the network. Numerical results, mostly
concerning low activity networks are discussed.Comment: 15 pages, Latex2e, 6 eps figure
Augmented Lagrangian Methods as Layered Control Architectures
For optimal control problems that involve planning and following a
trajectory, two degree of freedom (2DOF) controllers are a ubiquitously used
control architecture that decomposes the problem into a trajectory generation
layer and a feedback control layer. However, despite the broad use and
practical success of this layered control architecture, it remains a design
choice that must be imposed on the control policy. To address this
gap, this paper seeks to initiate a principled study of the design of layered
control architectures, with an initial focus on the 2DOF controller. We show
that applying the Alternating Direction Method of Multipliers (ADMM) algorithm
to solve a strategically rewritten optimal control problem results in solutions
that are naturally layered, and composed of a trajectory generation layer and a
feedback control layer. Furthermore, these layers are coupled via Lagrange
multipliers that ensure dynamic feasibility of the planned trajectory. We
instantiate this framework in the context of deterministic and stochastic
linear optimal control problems, and show how our approach automatically yields
a feedforward/feedback-based control policy that exactly solves the original
problem. We then show that the simplicity of the resulting controller structure
suggests natural heuristic algorithms for approximately solving nonlinear
optimal control problems. We empirically demonstrate improved performance of
these layered nonlinear optimal controllers as compared to iLQR, and highlight
their flexibility by incorporating both convex and nonconvex constraints
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