110,263 research outputs found
Optimal Control of Logically Constrained Partially Observable and Multi-Agent Markov Decision Processes
Autonomous systems often have logical constraints arising, for example, from
safety, operational, or regulatory requirements. Such constraints can be
expressed using temporal logic specifications. The system state is often
partially observable. Moreover, it could encompass a team of multiple agents
with a common objective but disparate information structures and constraints.
In this paper, we first introduce an optimal control theory for partially
observable Markov decision processes (POMDPs) with finite linear temporal logic
constraints. We provide a structured methodology for synthesizing policies that
maximize a cumulative reward while ensuring that the probability of satisfying
a temporal logic constraint is sufficiently high. Our approach comes with
guarantees on approximate reward optimality and constraint satisfaction. We
then build on this approach to design an optimal control framework for
logically constrained multi-agent settings with information asymmetry. We
illustrate the effectiveness of our approach by implementing it on several case
studies.Comment: arXiv admin note: substantial text overlap with arXiv:2203.0903
Distributed Optimization
We demonstrate a new framework for analyzing and controlling distributed systems, by solving constrained optimization problems with an algorithm based on that framework. The framework is ar. information-theoretic extension of conventional full-rationality game theory to allow bounded rational agents. The associated optimization algorithm is a game in which agents control the variables of the optimization problem. They do this by jointly minimizing a Lagrangian of (the probability distribution of) their joint state. The updating of the Lagrange parameters in that Lagrangian is a form of automated annealing, one that focuses the multi-agent system on the optimal pure strategy. We present computer experiments for the k-sat constraint satisfaction problem and for unconstrained minimization of NK functions
On tractability and congruence distributivity
Constraint languages that arise from finite algebras have recently been the
object of study, especially in connection with the Dichotomy Conjecture of
Feder and Vardi. An important class of algebras are those that generate
congruence distributive varieties and included among this class are lattices,
and more generally, those algebras that have near-unanimity term operations. An
algebra will generate a congruence distributive variety if and only if it has a
sequence of ternary term operations, called Jonsson terms, that satisfy certain
equations.
We prove that constraint languages consisting of relations that are invariant
under a short sequence of Jonsson terms are tractable by showing that such
languages have bounded relational width
KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development
Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system
Learning an Approximate Model Predictive Controller with Guarantees
A supervised learning framework is proposed to approximate a model predictive
controller (MPC) with reduced computational complexity and guarantees on
stability and constraint satisfaction. The framework can be used for a wide
class of nonlinear systems. Any standard supervised learning technique (e.g.
neural networks) can be employed to approximate the MPC from samples. In order
to obtain closed-loop guarantees for the learned MPC, a robust MPC design is
combined with statistical learning bounds. The MPC design ensures robustness to
inaccurate inputs within given bounds, and Hoeffding's Inequality is used to
validate that the learned MPC satisfies these bounds with high confidence. The
result is a closed-loop statistical guarantee on stability and constraint
satisfaction for the learned MPC. The proposed learning-based MPC framework is
illustrated on a nonlinear benchmark problem, for which we learn a neural
network controller with guarantees.Comment: 6 pages, 3 figures, to appear in IEEE Control Systems Letter
Two-Stage Subspace Constrained Precoding in Massive MIMO Cellular Systems
We propose a subspace constrained precoding scheme that exploits the spatial
channel correlation structure in massive MIMO cellular systems to fully unleash
the tremendous gain provided by massive antenna array with reduced channel
state information (CSI) signaling overhead. The MIMO precoder at each base
station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace
control matrix. The inner precoder is adaptive to the local CSI at each BS for
spatial multiplexing gain. The Tx subspace control is adaptive to the channel
statistics for inter-cell interference mitigation and Quality of Service (QoS)
optimization. Specifically, the Tx subspace control is formulated as a QoS
optimization problem which involves an SINR chance constraint where the
probability of each user's SINR not satisfying a service requirement must not
exceed a given outage probability. Such chance constraint cannot be handled by
the existing methods due to the two stage precoding structure. To tackle this,
we propose a bi-convex approximation approach, which consists of three key
ingredients: random matrix theory, chance constrained optimization and
semidefinite relaxation. Then we propose an efficient algorithm to find the
optimal solution of the resulting bi-convex approximation problem. Simulations
show that the proposed design has significant gain over various baselines.Comment: 13 pages, accepted by IEEE Transactions on Wireless Communication
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