Commercial software tools for intelligent autonomous systems

This article identifies some of the commercial software tools that can potentially be examined, or relied upon for their techniques, within new EPSRC projects entitled "Reconfigurable Autonomy" and "Distributed Sensing and Control.." awarded and to be undertaken between Liverpool, Southampton and Surrey Universities in the next 4 years. Although such projects strive to produce new techniques of various kinds, the software reviewed here could also influence, shape and help to integrate the algorithmic outcome of all 16 projects awarded within the EPSRC Autonomous and Intelligent Systems programme early 2012. To avoid mis-representation of technololgies provided by the software producer companies listed, most of this review is based on using quotes from original product descriptions

Toma de decisiones individuales y colectivas para sistemas multi-agente en entornos distribuidos

Esta línea de investigación se enfoca en mejorar las capacidades para la toma de decisiones individuales y colectivas de agentes en sistemas multi-agente. Dentro de este enfoque, se planea estudiar y desarrollar como mejorar en los agentes los siguientes aspectos: la capacidad de representación de conocimiento individual y colectivo, la capacidad de realizar inferencias, la capacidad de interacción e intercambio de información, y la capacidad de integrar esos elementos para tomar decisiones tanto individuales como colectivas. El aporte de esta investigación está orientado al desarrollo de formalismos y mecanismos para la toma de decisiones, por parte de agentes inteligentes deliberativos, en el contexto de un sistema multi-agente.Eje: Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informática (RedUNCI

Conocimiento compartido y razonamiento argumentativo colaborativo para entornos de múltiples agentes en ambientes distribuidos

Los Sistemas Multi-Agente constituyen un área en continuo crecimiento para el desarrollo de aplicaciones comerciales e industriales de gran escala ya que proveen de manera más natural soluciones a problemas complejos. En este tipo de sistemas, cada agente tiene capacidades limitadas e información incompleta sobre su entorno. Dicha información puede estar en contradicción con la información de otros agentes del sistema, y la resolución de este tipo de conflictos no es trivial. Esta línea de investigación se enfoca en mejorar las capacidades de razonamiento, representación de conocimiento, e interacción de agentes que participan en Sistemas Multi-Agente, los cuales colaboran y comparten su conocimiento en entornos dinámicos.Eje: Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informática (RedUNCI

Programación de agentes y argumentación

Esta línea de investigación involucra programación de agentes y argumentación. En particular, en este trabajo se presentan las motivaciones y las investigaciones en curso. El principal objetivo de esta linea, es el desarrollo de herramientas que permitan una programación declarativa de agentes inteligentes. En especial, agentes que utilizan razonamiento no monótono y de sentido común. Particularmente, se buscan herramientas que cuenten con mecanismos de argumentación para el razonamiento de los agentes. Actualmente se están estudiando diferentes arquitecturas de agente, que utilizan argumentación como mecanismo de razonamiento, y lenguajes de programación de agente, que permite implementaciones declarativas. Aprovechando este análisis y como primer paso para permitir que la programación de agente que razonan utilizando argumentación, se han presentado constructores que permitan formar argumentos para garantizar las creencias del agente.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Application of Hybrid Agents to Smart Energy Management of a Prosumer Node

We outline a solution to the problem of intelligent control of energy consumption of a smart building system by a prosumer planning agent that acts on the base of the knowledge of the system state and of a prediction of future states. Predictions are obtained by using a synthetic model of the system as obtained with a machine learning approach. We present case studies simulations implementing different instantiations of agents that control an air conditioner according to temperature set points dynamically chosen by the user. The agents are able of energy saving while trying to keep indoor temperature within a given comfort interval

Semantic Mutation Testing for Multi-Agent Systems

This paper introduces semantic mutation testing (SMT) into multiagent systems. SMT is a test assessment technique that makes changes to the interpretation of a program and then examines whether a given test set has the ability to detect each change to the original interpretation. These changes represent possible misunderstandings of how the program is interpreted. SMT is also a technique for assessing the robustness of a program to semantic changes. This paper applies SMT to three rule-based agent programming languages, namely Jason, GOAL and 2APL, provides several contexts in which SMT for these languages is useful, and proposes three sets of semantic mutation operators (i.e., rules to make semantic changes) for these languages respectively, and a set of semantic mutation operator classes for rule-based agent languages. This paper then shows, through preliminary evaluation of our semantic mutation operators for Jason, that SMT has some potential to assess tests and program robustness

Robust execution of BDI agent programs by exploiting synergies between intentions

A key advantage the reactive planning approach adopted by BDI-based agents is the ability to recover from plan execution failures, and almost all BDI agent programming languages and platforms provide some form of failure handling mechanism. In general, these consist of simply choosing an alternative plan for the failed subgoal (e.g., JACK, Jadex). In this paper, we propose an alternative approach to recovering from execution failures that relies on exploiting positive interactions between an agent’s intentions. A positive interaction occurs when the execution of an action in one intention assists the execution of actions in other intentions (e.g., by (re)establishing their preconditions). We have implemented our approach in a scheduling algorithm for BDI agents which we call SP. The results of a preliminary empirical evaluation of SP suggest our approach out- performs existing failure handling mechanisms used by state-of-the-art BDI languages. Moreover, the computational overhead of SP is modest

A Roadmap to Pervasive Systems Verification

yesThe complexity of pervasive systems arises from the many different aspects that such systems possess. A typical pervasive system may be autonomous, distributed, concurrent and context-based, and may involve humans and robotic devices working together. If we wish to formally verify the behaviour of such systems, the formal methods for pervasive systems will surely also be complex. In this paper, we move towards being able to formally verify pervasive systems and outline our approach wherein we distinguish four distinct dimensions within pervasive system behaviour and utilise different, but appropriate, formal techniques for verifying each one.EPSR