1,330 research outputs found
Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control
In this paper, we describe an integrated framework for autonomous decision
making in a dynamic and interactive environment. We model the interactions
between the ego agent and its operating environment as a two-player dynamic
game, and integrate cognitive behavioral models, Bayesian inference, and
receding-horizon optimal control to define a dynamically-evolving decision
strategy for the ego agent. Simulation examples representing autonomous vehicle
control in three traffic scenarios where the autonomous ego vehicle interacts
with a human-driven vehicle are reported.Comment: 2019 IEEE Conference on Decision and Contro
Collision Probabilities for Continuous-Time Systems Without Sampling [with Appendices]
Demand for high-performance, robust, and safe autonomous systems has grown
substantially in recent years. Fulfillment of these objectives requires
accurate and efficient risk estimation that can be embedded in core
decision-making tasks such as motion planning. On one hand, Monte-Carlo (MC)
and other sampling-based techniques can provide accurate solutions for a wide
variety of motion models but are cumbersome to apply in the context of
continuous optimization. On the other hand, "direct" approximations aim to
compute (or upper-bound) the failure probability as a smooth function of the
decision variables, and thus are widely applicable. However, existing
approaches fundamentally assume discrete-time dynamics and can perform
unpredictably when applied to continuous-time systems operating in the real
world, often manifesting as severe conservatism. State-of-the-art attempts to
address this within a conventional discrete-time framework require additional
Gaussianity approximations that ultimately produce inconsistency of their own.
In this paper we take a fundamentally different approach, deriving a risk
approximation framework directly in continuous time and producing a lightweight
estimate that actually improves as the discretization is refined. Our
approximation is shown to significantly outperform state-of-the-art techniques
in replicating the MC estimate while maintaining the functional and
computational benefits of a direct method. This enables robust, risk-aware,
continuous motion-planning for a broad class of nonlinear, partially-observable
systems.Comment: To appear at RSS 202
Behavioral Theory for Stochastic Systems? A Data-driven Journey from Willems to Wiener and Back Again
The fundamental lemma by Jan C. Willems and co-workers, which is deeply
rooted in behavioral systems theory, has become one of the supporting pillars
of the recent progress on data-driven control and system analysis. This
tutorial-style paper combines recent insights into stochastic and
descriptor-system formulations of the lemma to further extend and broaden the
formal basis for behavioral theory of stochastic linear systems. We show that
series expansions -- in particular Polynomial Chaos Expansions (PCE) of
-random variables, which date back to Norbert Wiener's seminal work --
enable equivalent behavioral characterizations of linear stochastic systems.
Specifically, we prove that under mild assumptions the behavior of the dynamics
of the -random variables is equivalent to the behavior of the dynamics of
the series expansion coefficients and that it entails the behavior composed of
sampled realization trajectories. We also illustrate the short-comings of the
behavior associated to the time-evolution of the statistical moments. The paper
culminates in the formulation of the stochastic fundamental lemma for linear
(descriptor) systems, which in turn enables numerically tractable formulations
of data-driven stochastic optimal control combining Hankel matrices in
realization data (i.e. in measurements) with PCE concepts.Comment: 30 pages, 8 figure
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
- …