Integration of a Contextual Observation System in a Multi-Process Architecture for Autonomous Vehicles

We propose a software layered architecture for autonomous vehicles whose efficiency is driven by pull-based acquisition of sensor data. This multiprocess software architecture, to be embedded into the control loop of these vehicles, includes a Belief-Desire-Intention agent that can consistently assist the achievement of intentions. Since driving on roads implies huge dynamic considerations, we tackle both reactivity and context awareness considerations on the execution loop of the vehicle. While the proposed architecture gradually offers 4 levels of reactivity, from arch-reflex to the deep modification of the previously built execution plan, the observation module concurrently exploits noise filtering and introduces frequency control to allow symbolic feature extraction while both fuzzy and first order logic management are used to enforce consistency and certainty over the context information properties. The presented use-case, the daily delivery of a network of pharmacy offices by an autonomous vehicle taking into account contextual (spatio-temporal) traffic features, shows the efficiency and the modularity of the architecture, as well as the scalability of the reaction levels

A multi-agent approach for ambient system design : a formal model incorporating planning and learning

Ce travail présente une architecture logicielle concrète dédiée aux besoins et caractéristiques des systèmes d'Intelligence Ambiante (AmI). Le modèle comportemental proposé, appelé Higher-order Agent (HoA), capture simultanément l'évolution de l'état mental de l'agent ainsi que l'état de son plan d'actions. Les expressions du plan sont écrites et composées en utilisant un langage algébrique formel, nommé AgLOTOS. Les plans sont construits automatiquement et à la volée, comme un système de processus concurrents, déduits des intentions de l'agent et de ses préférences d'exécution. Basé sur une sémantique de plans et d'actions concurrentes, un service de guidance est aussi proposé afin d'assister l'agent dans le choix de ses prochaines exécutions. Cette guidance permet d'améliorer la satisfaction des intentions de l'agent au regard des plans concurrents possibles et en fonction du contexte actuel de l'agent. La "localité" et le "temps" étant considérés comme des informations contextuelles clés dans l'activité de l'agent, nous les prenons en compte au travers de deux fonctions utilitaires originales conçues à partir des expériences des exécutions d'action et pouvant être combinées suivant les préférences stratégiques de l'agent. La structure compositionnelle des expressions AgLOTOS est mise à profit pour permettre des révisions ciblées du plan de l'agent, Les révisions des sous-plans sont donc réalisées automatiquement en fonction des mises à jour apportées aux intentions, tout en maintenant la consistance du comportement de l'agent. Un cas d'étude est développé afin de montrer comment l'agent peut agir, même s'il subit des changements inattendus de son contexte, en fonction de ses expériences passées qui révèlent certains cas de d'échecs.This work presents a concrete software architecture dedicated to ambient intelligence (AmI) features and requirements. The proposed behavioral model, called Higher-order Agent (HoA) captures the evolution of the mental representation of the agent and the one of its plan simultaneously. Plan expressions are written and composed using a formal algebraic language, namely AgLOTOS, so that plans are built automatically and on the fly, as a system of concurrent processes. Due to the compositional structure of AgLOTOS expressions, the updates of sub-plans are realized automatically accordingly to the revising of intentions, hence maintaining the consistency of the agent. Based on a specific semantics, a guidance service is also proposed to assist the agent in its execution. This guidance allows to improve the satisfaction of the agent's intentions with respect to the possible concurrent plans and the current context of the agent. Adopting the idea that "location" and "time" are key stones information in the activity of the agent, we show how to enforce guidance by ordering the different possible plans. As a major contribution, we demonstrate two original utility functions that are designed from the past-experiences of the action executions, and that can be combined accordingly to the current balance policy of the agent. A use case scenario is developed to show how the agent can act, even if it suffers from unexpected changes of contexts, it does not have many experiences and whose past experiences reveals some failure cases

Une approche multi-agent pour la conception de systèmes d'intelligence ambiante : un modèle formel intégrant planification et apprentissage

This work presents a concrete software architecture dedicated to ambient intelligence (AmI) features and requirements. The proposed behavioral model, called Higher-order Agent (HoA) captures the evolution of the mental representation of the agent and the one of its plan simultaneously. Plan expressions are written and composed using a formal algebraic language, namely AgLOTOS, so that plans are built automatically and on the fly, as a system of concurrent processes. Due to the compositional structure of AgLOTOS expressions, the updates of sub-plans are realized automatically accordingly to the revising of intentions, hence maintaining the consistency of the agent. Based on a specific semantics, a guidance service is also proposed to assist the agent in its execution. This guidance allows to improve the satisfaction of the agent's intentions with respect to the possible concurrent plans and the current context of the agent. Adopting the idea that "location" and "time" are key stones information in the activity of the agent, we show how to enforce guidance by ordering the different possible plans. As a major contribution, we demonstrate two original utility functions that are designed from the past-experiences of the action executions, and that can be combined accordingly to the current balance policy of the agent. A use case scenario is developed to show how the agent can act, even if it suffers from unexpected changes of contexts, it does not have many experiences and whose past experiences reveals some failure cases.Ce travail présente une architecture logicielle concrète dédiée aux besoins et caractéristiques des systèmes d'Intelligence Ambiante (AmI). Le modèle comportemental proposé, appelé Higher-order Agent (HoA), capture simultanément l'évolution de l'état mental de l'agent ainsi que l'état de son plan d'actions. Les expressions du plan sont écrites et composées en utilisant un langage algébrique formel, nommé AgLOTOS. Les plans sont construits automatiquement et à la volée, comme un système de processus concurrents, déduits des intentions de l'agent et de ses préférences d'exécution. Basé sur une sémantique de plans et d'actions concurrentes, un service de guidance est aussi proposé afin d'assister l'agent dans le choix de ses prochaines exécutions. Cette guidance permet d'améliorer la satisfaction des intentions de l'agent au regard des plans concurrents possibles et en fonction du contexte actuel de l'agent. La "localité" et le "temps" étant considérés comme des informations contextuelles clés dans l'activité de l'agent, nous les prenons en compte au travers de deux fonctions utilitaires originales conçues à partir des expériences des exécutions d'action et pouvant être combinées suivant les préférences stratégiques de l'agent. La structure compositionnelle des expressions AgLOTOS est mise à profit pour permettre des révisions ciblées du plan de l'agent, Les révisions des sous-plans sont donc réalisées automatiquement en fonction des mises à jour apportées aux intentions, tout en maintenant la consistance du comportement de l'agent. Un cas d'étude est développé afin de montrer comment l'agent peut agir, même s'il subit des changements inattendus de son contexte, en fonction de ses expériences passées qui révèlent certains cas de d'échecs

On the Fly PSO Inspired Algorithm For Graph Distribution

International audienceThis paper deals with clustering applications, it proposes an on the fly algorithm for distributing graphs. The proposed algorithm is based on the meta-heuristic "Particle Swarm Optimization (PSO)". For the sake of presentation, the idea of adapting the PSO algorithm for graphs distribution is developed firstly by assuming that the graph is already generated. After that a distributed algorithm is proposed for concurrently generating and distributing graphs. This distribution should ensure the workload balancing property and the minimization of the number of distributed inter-site edges

Contextual-Timed Planning Management for Ambient Systems

International audienceIn this paper, we propose an algebraic language, called Time-AgLOTOS, which is dedicated to express BDI agent plans, according to the features and requirements of Ambient Intelligence (AmI) systems. Plan expressions are written and composed using Time-AgLOTOS, so that plans are built automatically and on the fly, as a system of concurrent processes. This language describes the time-dependent behavior of the agent and provides a theoretical foundation for performing planning under timing constraints, taking into account the duration of actions. Also, it allows to express behavioral capabilities such as communication, mobility and cooperation. In this context, we show how to achieve a powerful mechanism for a contextual guidance based on a specific and formal construction called Contextual Time Planning System (CTPS)

Learning from situated experiences for a contextual planning guidance

International audienceThis paper presents AgLOTOS as an algebraic language dedicated to the specification of agent plans in ambient systems. AgLOTOS offers two levels of plans: elementary plans which are composed to produce an intention plan; The intention plans which are, in turn, composed to build an agent plan. The composition relies on several operators such as alternative and concurrency. Consequently, the plans can be built automatically as a system of concurrent processes. At the execution level, our approach helps the agent to select an optimal plan preserving the consistency of its intentions. The selection is based on an original and formal construction called contextual planning system (CPS), which presents the potential paths with the associated contexts while removing the inconsistent options. Finally, our CPS is improved by using past-experiences for a better guidance of the agent

A Dynamical Plan Revising for Ambient Systems

pp. 37-44International audienceThe proposed AgLOTOS formal specification language is dedicated to express BDI agent plans, according to the features and requirements of Ambient Intelligence (AmI). It offers a rich modular approach to express and compose elementary plans in order to execute them concurrently. We show how a plan is built automatically as a system of concurrent processes from the mental attitudes of the agent. In contrast to existing approaches, the plan is viewed as the realization of a whole set of partially ordered intentions. The AgLOTOS semantics accords with the possibility of updating some sub-plans on the fly, as the intention set of the BDI agent is revised

Improving the Contextual Selection of BDI Plans by Incorporating Situated Experiments

International audienceThis paper is dedicated to intentional BDI agents evolving in ambient environment. The planning management framework we propose, looks for efficient guidance to improve the satisfaction of the agent's intentions with respect to the possible concurrent plans and the current context of the agent. Adopting the idea that " location " and " time " are key stones information in the activity of the agent, we show how to enforce guidance by ordering the different possible plans. As a major contribution, we demonstrate two original utility functions that are designed from the past-experiences of the action executions, and that can be combined accordingly to the current balance policy of the agent