Autonomous Control & Learning Systems

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Abstractions about systems that plan, learn or self-organize their own behavior, covering control-theoretic tools (Control-Lyapunov Function, LQR-RRT, Pullback Attractor), autonomous and self-organizing computing (Organic Computing, Vehicular Automation, Action Model Learning), and equilibrium or verification concepts like Wardrop Equilibrium and Symbolic Trajectory Evaluation.

11 abstractions in this family — domain-specific abstractions that sit near one another in structural-signature space (k-means over structural-signature embeddings). Each is shown with its short description.

• Action model learning — Action model learning (sometimes abbreviated action learning) is an area of machine learning concerned with the creation and modification of a software agent's knowledge about the effects and preconditions of the actions that can be executed within its environment.
• Behavioral modeling — The main object in the behavioral setting is the behavior – the set of all signals compatible with the system.
• Control-Lyapunov function — In control theory, a control-Lyapunov function (CLF) is an extension of the idea of Lyapunov function V(x) to systems with control inputs.
• Fractional-Order System — A dynamical model whose governing relation uses a specified noninteger-order temporal operation.
• Linear-quadratic regulator rapidly exploring random tree — Linear-quadratic regulator rapidly exploring random tree (LQR-RRT) is a sampling based algorithm for kinodynamic planning.
• Organic computing — Organic computing designs autonomous computing systems with self-organizing properties such as self-configuration, self-optimization, self-healing, and self-protection.
• Pullback attractor — Importantly, in the case of a deterministic dynamical system (one without noise), the pullback limit coincides with the deterministic forward limit, so it is meaningful to compare deterministic and random omega-limit sets, attractors, and so forth.
• Symbolic trajectory evaluation — Symbolic trajectory evaluation (STE) is a lattice-based model checking technology that uses a form of symbolic simulation.
• Tunnel problem — The tunnel problem is a philosophical thought experiment first introduced by Jason Millar in 2014.
• Vehicular automation — Vehicular automation is using technology to assist or replace the operator of a vehicle such as a car, truck, aircraft, rocket, military vehicle, or boat.
• Wardrop Equilibrium — In game theory and operations research, a Wardrop equilibrium is a concept developed by John Glen Wardrop for the prediction of traffic patterns in transportation networks that are subject to congestion.