State space generation suffers from the typical combinatorial explosion problem when dealing with industrial specifications. In particular, memory consumption while storing the state space must be tackled to verify safety properties. Decision Diagrams are a way to tackle this problem. However, their performance strongly rely on the way variables encode a system. Another way to fight combinatori...

This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-v...

In contrast to supervised learning, RL agents are not given instructive feedback on what the best decision in a particular situation is. This leads to the temporal credit assignment problem, that is, the problem of determining which part of the behaviour deserves the reward (Sutton, 1984). To address this issue, the iterative approach to RL applies backpropagation of the value function in the s...

Reinforcement Learning (RL) is a machine learning paradigm with which autonomous agents can improve their behavior in unknown environments based on their own experience without an explicit teacher signal. RL algorithms are based on estimating a value function over the state space, and scaling them to large state spaces remains a challenge. One approach, known as variable resolution, is to focus...

This paper gives an abstract sufficient condition on Riesz basis with parentheses property for the generators of C0-groups in Hilbert spaces whose eigenvalues are comprised of some finite unification of separable sets after taking the algebraic multiplicities into account. The condition is then applied to the closed-loop system of a serially connected string system under joint damping feedbacks...

Real-world applications, such as diagnosis and embedded control, are increasingly being framed as OpSAT problems problems of finding the best solution that satisfies a formula in propositional state logic. Previous methods, such as Conflict-directed A*, solve OpSAT problems through a weak coupling of A* search, used to generate optimal candidates, and a DPLL-based SAT solver, used to test feasi...

In the setting of collaborative live coding, there emerges a number of issues: (1) the need for communication, (2) issues of conflicts in sharing program state space, and (3) remote control of code execution. This paper proposes solutions to these problems. In the recent extension of UrMus a programming environment for mobile music application developmentwe introduce a paradigm of shared and in...

This article describes an optimal control algorithm for a DC motor drive operating as a positioning system. The control algorithm is based on combination of artificial neural network and state space method with variable gain. The positioning system operating under proposed algorithm has demonstrated a transient process close to optimal without overshoot. It has been also shown that the control ...

We consider networks of finite-state machines having local transitions conditioned by the current state of other automata. In this paper, we introduce a reduction procedure tailored for reachability properties of the form “from global state s, there exists a sequence of transitions leading to a state where an automaton g is in a local state ⊤”. By analysing the causality of transitions within t...