Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

What is an intelligent system?

The concept of intelligent system has emerged in information technology as a type of system derived from successful applications of artificial intelligence. The goal of this paper is to give a general description of an intelligent system, which integrates previous approaches and takes into account recent advances in artificial intelligence. The paper describes an intelligent system in a generic way, identifying its main properties and functional components, and presents some common categories. The presented description follows a practical approach to be used by system engineers. Its generality and its use is illustrated with real-world system examples and related with artificial intelligence methods

Autonomous Search and Rescue with Modeling and Simulation and Metrics

Unmanned Aerial Vehicles (UAVs) provide rapid exploration capabilities in search and rescue missions while accepting more risks than human operations. One limitation in that current UAVs are heavily manpower intensive and such manpower demands limit abilities to expand UAV use. In operation, manpower demands in UAVs range from determining tasks, selecting waypoints, manually controlling platforms and sensors, and tasks in between. Often, even a high level of autonomy is possible with human generated objectives and then autonomous resource allocation, routing, and planning. However, manually generating tasks and scenarios is still manpower intensive. To reduce manpower demands and move towards more autonomous operations, the authors develop an adaptive planning system that takes high level goals from a human operator and translates them into situationally relevant tasking. For expository simulation, the authors further describe constructing a scenario around the 2018 Hawaii Puna lava natural disaster

Incorporating temporal-bounded CBR techniques in real-time agents

Nowadays, MAS paradigm tries to move Computation to a new level of abstraction: Computation as interaction, where large complex systems are seen in terms of the services they offer, and consequently in terms of the entities or agents providing or consuming services. However, MAS technology is found to be lacking in some critical environments as real-time environments. An interaction-based vision of a real-time system involves the purchase of a responsibility by any entity or agent for the accomplishment of a required service under possibly hard or soft temporal conditions. This vision notably increases the complexity of these kinds of systems. The main problem in the architecture development of agents in real-time environments is with the deliberation process where it is difficult to integrate complex bounded deliberative processes for decision-making in a simple and efficient way. According to this, this work presents a temporal-bounded deliberative case-based behaviour as an anytime solution. More specifically, the work proposes a new temporal-bounded CBR algorithm which facilitates deliberative processes for agents in real-time environments, which need both real-time and deliberative capabilities. The paper presents too an application example for the automated management simulation of internal and external mail in a department plant. This example has allowed to evaluate the proposal investigating the performance of the system and the temporal-bounded deliberative case-based behaviour. 2010 Elsevier Ltd. All rights reserved.This work is supported by TIN2006-14630-C03-01 projects of the Spanish government, GVPRE/2008/070 project, FEDER funds and CONSOLIDER-INGENIO 2010 under Grant CSD2007-00022.Navarro Llácer, M.; Heras Barberá, SM.; Julian Inglada, VJ.; Botti Navarro, VJ. (2011). Incorporating temporal-bounded CBR techniques in real-time agents. Expert Systems with Applications. 38(3):2783-2796. https://doi.org/10.1016/j.eswa.2010.08.070S2783279638

The Challenges of Real-Time AI

The research agendas of two major areas of computer science are converging: Artificial Intelligence (AI) methods are moving towards more realistic domains requiring real-time responses, and real-time systems are moving towards more complex applications requiring intelligent behavior. Together, they meet at the crossroads of interest in "real-time intelligent control," or "real-time AI." This subfield is still being defined by the common interests of researchers from both real-time and AI systems. As a result, the precise goals for various real-time AI systems are still in flux. This paper describes an organizing conceptual structure for current real-time AI research, clarifying the different meanings this term has acquired for various researchers. Having identified the various goals of real-time AI research, we then specify some of the necessary steps towards reaching those goals. This in turn enables us to identify promising areas for future research in both AI and real-time systems techniques. (Also cross-referenced as UMIACS-TR-94-69

Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

The scarcity and diversity of resources among the devices of heterogeneous computing environments may affect their ability to perform services with specific Quality of Service constraints, particularly in dynamic distributed environments where the characteristics of the computational load cannot always be predicted in advance. Our work addresses this problem by allowing resource constrained devices to cooperate with more powerful neighbour nodes, opportunistically taking advantage of global distributed resources and processing power. Rather than assuming that the dynamic configuration of this cooperative service executes until it computes its optimal output, the paper proposes an anytime approach that has the ability to tradeoff deliberation time for the quality of the solution. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial solution and effectively optimise the rate at which the quality of the current solution improves at each iteration, with an overhead that can be considered negligible

A cooperative architecture for target localization using underwater vehicles

Nous nous intéressons à l'architecture de robots marins et sous-marins autonomes dans le cadre de missions nécessitant leur coopération. Cette coopération s'avère difficile du fait que la communication (acoustique) est très contrainte en termes de débit et de portée.  Notre travail se place dans le contexte de missions d'exploration pour détecter des éléments particuliers sur les fonds marins, et en particulier des sources d'eau chaude. Pour cela, le véhicule sous-marin parcours des segments de droite pré-planifiés et rejoint des points de rendez-vous (points de communication). Ces derniers permettent d'assurer le suivi de bon déroulement de la mission, mais surtout de mettre en oeuvre des schémas de coopération entre les véhicules sous-marins. Au fur et à mesure de l'exploration, les sous-marins construisent et mettent à jour une représentation de l'environnement qui décrit les probabilités de localisation de sources. Une approche adaptative exploite ces informations et permet de dévier les sous-marins de leurs plan initial pour augmenter la quantité d'information, tout en respectant les contraintes du plan initial, et en particulier les rendez-vous de communication. Lors des rendez-vous, chaque véhicule échange ses données avec les autres, en ne transmettant que les informations nécessaires à la mise en place de schémas de coopération. L'ensemble de ces fonctions sont intégrées au sein de l'architecture existante T-REX, pour laquelle nous proposons des composants supplémentaires qui permettent la cartographie des fonds et la définition de schémas de coopération. Différentes simulations permettent d'évaluer les travaux proposés. ABSTRACT : There is a growing research interest in Autonomous Underwater Vehicles (AUV), due to the need for increasing our knowledge about the deep sea and understanding the effects the human way of life has on it. This need has pushed the development of new technologies to design more efficient and more autonomous underwater vehicles. Autonomy refers, in the context of this thesis, to the “decisional autonomy”, i.e. the capability of taking decisions, in uncertain, varying and unknown environments. A more recent concern in AUV area is to consider a fleet of vehicles (AUV, ASV, etc). Indeed, multiple vehicles with heterogeneous capabilities have several advantages over a single vehicle system, and in particular the potential to accomplish tasks faster and better than a single vehicle. Underwater target localization using several AUVs (Autonomous Underwater Vehicles) is a challenging issue. A systematic and exhaustive coverage strategy is not efficient in term of exploration time: it can be improved by making the AUVs share their information and cooperate to optimize their motions. The contribution of this thesis is the definition of an architecture that integrates such a strategy that adapts each vehicle motions according to its and others’ sensory information. Communication points are required to make underwater vehicles exchange information : for that purpose the system involves one ASV (Autonomous Surface Vehicle), that helps the AUVs re-localize and exchange data, and two AUVs that adapt their strategy according to gathered information, while satisfying the associated communication constraints. Each AUV is endowed with a sensor that estimates its distance with respect to targets, and cooperates with others to explore an area with the help of an ASV. To provide the required autonomy to these vehicles, we build upon an existing system (T-REX) with additional components, which provides an embedded planning and execution control framework. Simulation results are carried out to evaluate the proposed architecture and adaptive exploration strategy

A software architecture for autonomous spacecraft

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaf 47).by Jimmy S. Shih.M.Eng