Physics-Informed Neural Nets for Control of Dynamical Systems

Physics-informed neural networks (PINNs) impose known physical laws into the learning of deep neural networks, making sure they respect the physics of the process while decreasing the demand of labeled data. For systems represented by Ordinary Differential Equations (ODEs), the conventional PINN has a continuous time input variable and outputs the solution of the corresponding ODE. In their original form, PINNs do not allow control inputs neither can they simulate for long-range intervals without serious degradation in their predictions. In this context, this work presents a new framework called Physics-Informed Neural Nets for Control (PINC), which proposes a novel PINN-based architecture that is amenable to \emph{control} problems and able to simulate for longer-range time horizons that are not fixed beforehand. The framework has new inputs to account for the initial state of the system and the control action. In PINC, the response over the complete time horizon is split such that each smaller interval constitutes a solution of the ODE conditioned on the fixed values of initial state and control action for that interval. The whole response is formed by feeding back the predictions of the terminal state as the initial state for the next interval. This proposal enables the optimal control of dynamic systems, integrating a priori knowledge from experts and data collected from plants into control applications. We showcase our proposal in the control of two nonlinear dynamic systems: the Van der Pol oscillator and the four-tank system

Louis Halphen (1880-1950)

Latouche Robert. Louis Halphen (1880-1950). In: Bibliothèque de l'école des chartes. 1951, tome 109, livraison 2. pp. 371-376

AgentSpeak(ER): An Extension of AgentSpeak(L) improving Encapsulation and Reasoning about Goals

The 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018)In this paper we introduce AgentSpeak(ER), an extension of the AgentSpeak(L) language tailored to support encapsulation. The AgentSpeak(ER) extension aims at improving the style of BDI agent programming along relevant aspects, including program modularity and readability, failure handling, and reactive as well as goal-based reasoning

Aplib: Tactical Agents for Testing Computer Games

Modern interactive software, such as computer games, employ complex user interfaces. Although these user interfaces make the games attractive and powerful, unfortunately they also make them extremely difficult to test. Not only do we have to deal with their functional complexity, but also the fine grained interactivity of their user interface blows up their interaction space, so that traditional automated testing techniques have trouble handling it. An agent-based testing approach offers an alternative solution: agents’ goal driven planning, adaptivity, and reasoning ability can provide an extra edge towards effective navigation in complex interaction space. This paper presents aplib, a Java library for programming intelligent test agents, featuring novel tactical programming as an abstract way to exert control over agents’ underlying reasoning-based behavior. This type of control is suitable for programming testing tasks. Aplib is implemented in such a way to provide the fluency of a Domain Specific Language (DSL). Its embedded DSL approach also means that aplib programmers will get al.l the advantages that Java programmers get: rich language features and a whole array of development tools

Balancing Decentralization for Restoration in Power Distribution Systems With Agents

Power distribution systems are subject to faults that may cause service interruptions. The outage impact can be attenuated by restoration procedures, aiming to reconfigure the grid until faulted components are repaired. Proposals for these procedures are usually either fully centralized or fully decentralized. In this paper, a hybrid approach (partially decentralized) for the restoration problem based on a multi-agent system (MAS) is proposed and evaluated. Results show that caution must be taken when specifying the level of decentralization achieved by agent based procedures devoted to system restoration, especially if low power devices are utilized to achieve agent communication