A model of anytime algorithm performance for bi-objective optimization

International audienceAnytime algorithms allow a practitioner to trade-off runtime for solution quality. This is of particular interest in multi-objective combinatorial optimization since it can be infeasible to identify all efficient solutions in a reasonable amount of time. We present a theoretical model that, under some mild assumptions, characterizes the “optimal” trade-off between runtime and solution quality, measured in terms of relative hypervolume, of anytime algorithms for bi-objective optimization. In particular, we assume that efficient solutions are collected sequentially such that the collected solution at each iteration maximizes the hypervolume indicator, and that the non-dominated set can be well approximated by a quadrant of a superellipse. We validate our model against an “optimal” model that has complete knowledge of the non-dominated set. The empirical results suggest that our theoretical model approximates the behavior of this optimal model quite well. We also analyze the anytime behavior of an ε-constraint algorithm, and show that our model can be used to guide the algorithm and improve its anytime behavior

Optimization and Control of Cyber-Physical Vehicle Systems

A cyber-physical system (CPS) is composed of tightly-integrated computation, communication and physical elements. Medical devices, buildings, mobile devices, robots, transportation and energy systems can benefit from CPS co-design and optimization techniques. Cyber-physical vehicle systems (CPVSs) are rapidly advancing due to progress in real-time computing, control and artificial intelligence. Multidisciplinary or multi-objective design optimization maximizes CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modeling errors and uncertainties. CPVS optimization occurs at design-time and at run-time. This paper surveys the run-time cooperative optimization or co-optimization of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilized in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimization and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined

Modeling Intelligent Control of Distributed Cooperative Inferencing

The ability to harness different problem-solving methods together into a cooperative system has the potential for significantly improving the performance of systems for solving NP-hard problems. The need exists for an intelligent controller that is able to effectively combine radically different problem-solving techniques with anytime and anywhere properties into a distributed cooperative environment. This controller requires models of the component algorithms in conjunction with feedback from those algorithms during run-time to manage a dynamic combination of tasks effectively. This research develops a domain-independent method for creating these models as well as a model for the controller itself. These models provide the means for the controller to select the most appropriate algorithms, both initially and during run-time. We utilize the algorithm performance knowledge contained in the algorithm models to aid in the selection process. This methodology is applicable to many NP-hard problems; applicability is only limited by the availability of anytime and anywhere algorithms for that domain. We demonstrate the capabilities of this methodology by applying it to a known NP-hard problem: uncertain inference over Bayesian Networks. Experiments using a collection of randomly generated networks and some common inference algorithms showed very promising results. Future directions for this research could involve the analysis of the impact of the accuracy of the algorithm models on the performance of the controller; the issue is whether the increased model accuracy would cause excessive system overhead, counteracting the potential increase in performance due to better algorithm selection

Anytime Algorithmに基づいたリアルタイム画像処理のための 適応的スケジューリングに関する研究

愛知県立大学2013年

Meta-level Control in Multi-Agent Systems.

Abstract Sophisticated agents operating in open environments must make decisions that efficiently trade off the use of their limited resources between dynamic deliberative actions and domain actions? This is the meta-level control problem for agents operating in resource-bounded multi-agent environments. Control activities involve decisions on when to invoke and the amount to effort to put into scheduling and coordination of domain activities. The focus of this paper is how to make effective meta-level control decisions. We show that meta-level control with bounded computational overhead allows complex agents to solve problems more efficiently than current approaches in dynamic open multi-agent environments. The meta-level control approach that we present is based on the decision-theoretic use of an abstract representation of the agent state. This abstraction concisely captures critical information necessary for decision making while bounding the cost of meta-level control and is appropriate for use in automatically learning the meta-level control policies

Tuning the Computational Effort: An Adaptive Accuracy-aware Approach Across System Layers

This thesis introduces a novel methodology to realize accuracy-aware systems, which will help designers integrate accuracy awareness into their systems. It proposes an adaptive accuracy-aware approach across system layers that addresses current challenges in that domain, combining and tuning accuracy-aware methods on different system layers. To widen the scope of accuracy-aware computing including approximate computing for other domains, this thesis presents innovative accuracy-aware methods and techniques for different system layers. The required tuning of the accuracy-aware methods is integrated into a configuration layer that tunes the available knobs of the accuracy-aware methods integrated into a system

Heurísticas para el control deliberativo en una arquitectura de agentes inteligentes de tiempo real

El área de la Inteligencia Artificial está experimentado un gran avance en los últimos tiempos con su aplicación a un mayor número de campos diferentes. Uno de ellos es el de los problemas de tiempo real. Problemas donde no sólo es importante la lógica del cálculo de las soluciones, sino también el instante de tiempo en que son calculadas dichas soluciones. Este acercamiento entre ambas árear es, en principio, provechoso, pues la Inteligencia Artificial puede aportar nuevas posibilidades a los sistemas de tiempo real, como una mayor flexibilidad de adaptación a entornos complejos y dinámicos. Sin embargo esta aproximación ha presentado desde siempre importantes dificultades. Principalmente los sistemas de tiempo real poseen unos requerimientos temporales (predecibilidad de los tiempos de respuesta principalmente) que no suelen ser habituales en las técnicas de Inteligencia Artificial. Entre otras formas de abordar este problema, está el desarrollo de arquitecturas software para el diseño de agentes inteligentes para su uso en entornos de tiempo real. Estas arquitecturas poseen diferentes mecanismos para que los agentes construidos puedan trabajar en entornos de tiempo real . Estas arquitecturas poseen diferentes mecanismos para que los agentes construidos puedan trabajas en entornos de tiempo real ofreciendo comportamientos reactivos (para cumplir los requerimientos temporales) y deliberativos (que hacen uso de técnicas de Inteligencia Artificial para conseguir mejores prestaciones). Una de estas arquitecturas es ARTIS. Esta arquitectura hace uso de una planificación de sus tareas a dos niveles para conseguir complir sus objetivos. Por un lado un planificador de primer nivel garantiza la obtención de respuestas dentro de límites temporales estrictos. Un planificador de segundo nivel se encarga del control de componentes que mejoran la calidad de los resultados. El trabajo presentado se centra en este segundo planificador, desarrollando dos heurísticas, SSS.......Hérnandez López, L. (2004). Heurísticas para el control deliberativo en una arquitectura de agentes inteligentes de tiempo real [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2671Palanci

Relevanzbasierte Informationsbeschaffung für die informierte Entscheidungsfindung intelligenter Agenten

This dissertation introduces relevance-based information acquisition for intelligent software agents based on Howard s information value theory and decision networks. Active information acquisition is crucial in domains with partial observability in order to establish situation awareness of autonomous systems for deliberate decisions. The new semi-myopic approach addresses the complexity challenge of decision-theoretic relevance computation by reducing the set of variables to be evaluated in the first place. Links in a decision network encode stochastic dependencies of variables. Through utility dependency analysis using Pearl s d-separation criterion, the set of relevant variables can be efficiently reduced to a proven minimum without actually computing information value. In addition to an implementation with detailed runtime performance analysis, the applicability of the approach is shown in the domain of intelligent logistics control