Remote Agent: to boldly go where no AI system has gone before

Muscettola, Nicola; Nayak, P. Pandurang; Pell, Barney; Williams, Brian

doi:10.1016/s0004-3702(98)00068-x

Cited by 442 publications

(216 citation statements)

References 23 publications

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“…They are used in many areas, including ecommerce [14], industrial process control [4], web services [17] and autonomous vehicles [18]. Moreover, as computational platforms and wireless networking becomes ubiquitous, and software artifacts will increasingly be able to migrate through large physical/virtual distances, access vast amounts of information, spawn new computations on a wide variety of platforms, and so on, so agentbased techniques for mobile and ubiquitous systems will provide such software artifacts with autonomous and 'intelligent' behaviour to cater for their dynamic environment, requirements and knowledge.…”

Section: Discussionmentioning

confidence: 99%

Programming Groups of Rational Agents

Fisher

Ghidini²,

Hirsch

2004

Lecture Notes in Computer Science

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Abstract. In this paper, we consider the problem of effectively programming groups of agents. These groups should capture structuring mechanisms common in multi-agent systems, such as teams, cooperative groups, and organisations. Not only should individual agents be dynamic and evolving, but the groups in which the agents occur must be open, flexible and capable of similar evolution and restructuring. We enable the description and implementation of such groups by providing an extension to our previous work on programming languages for agent-based systems based on executable temporal and modal logics. With such formalism as a basis, we consider the grouping aspects within multi-agent systems. In particular, we describe how this logic-based approach to grouping has been implemented in Java and consider how this language can be used for developing multi-agent systems.

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Section: Discussionmentioning

confidence: 99%

Programming Groups of Rational Agents

Fisher

Ghidini²,

Hirsch

2004

Lecture Notes in Computer Science

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“…Conceptually closely related to it is the mission planning module for the autonomous space probe Deep Space One [194]. Both systems are constraint-based and generate schedules for parametrized plan fragments, which have to obey larger sets of mission critical constraints.…”

Section: Ai Scheduling In a Nutshellmentioning

confidence: 99%

“…But as pointed out by several authors ( [180], amongst others), domain models need to have a declarative semantics independent of domain heuristics or planning algorithm. The reasons for this do not only lie in theoretical properties like a sound foundation and explicit consistency criteria but they are also a result of experience with fielded planning systems, for instance, as reported in [194]. Setting up and maintaining consistency in planning domain models becomes a serious problem in any mission-critical application and plays a major role not only for the question whether or not all plans that are generated by the respective system are correct, but also for cases in which (unexpectedly) no plan can be found [57].…”

Section: Declarative Semantics and Consistencymentioning

confidence: 99%

Hybrid Planning and Scheduling

Schattenberg¹

2015

Künstl Intell

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PrefacePlanning and scheduling are well-established disciplines in the field of Artificial Intelligence. They provide flexibility, robustness, and effectiveness to complex software systems in a variety of application areas. While planning is the process of finding a course of action that achieves a goal or performs a specified task, scheduling deals with the assignment of resources and time to given activities, taking into account resource restrictions and temporal dependencies. In other words, planning focuses on reasoning about causal structures and identifying the necessary actions for achieving a specific goal; scheduling concentrates on resource consumption and production for optimizing a coherent parameter assignment of a plan. As successful these techniques clearly are, the actual demands of complex, real-world applications go far beyond the potential of these single methods, however. They require an adequate integration of these problem solving methods as well as a combination of different planning and scheduling paradigms. Particularly important are abstraction-based, hierarchical approaches because of both their expressive knowledge representation and their efficiency in finding solutions. Current state-of-the-art systems rarely address the question of method integration; isolated approaches do so only in ad hoc implementations and mostly lack a proper formal basis.This thesis presents a formal framework for plan and schedule generation based on a well-founded conceptualization of refinement planning: An abstract problem specification is transformed stepwise into a concrete, executable solution. In each refinement step, plan deficiencies identify faulty or under-developed parts of the plan, which in turn triggers the generation of transformation operators that try to resolve them. All involved entities are explicitly represented and therefore transparent to the framework. This property allows for two novel aspects of our approach: First, any planning and scheduling methodology can be functionally decomposed and mapped on the deficiency announcement and plan transformation generation principle, and second, the framework allows for an explicit declaration of planning strategies. We first investigate the flexibility of the extremely modular system design by instantiating the framework in a variety of system configurations including classical partial-order causal-link (POCL) planning, hierarchical task-network (HTN) planning, and classical scheduling.As a key feature, the presented approach provides a formally integrated treatment of action and state abstraction, thus naturally combining causality-focused reasoning with hierarchical, procedure-oriented methods. While the use of procedural knowledge allows to rely on well-known, predefined solutions to planning problems, the non-hierarchical methods provide the flexibility to come up with non-standard solutions and to complete under-specified problem instances, respectively. The resulting technique of hybrid planning is capable of constructing a plan's caus...

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“…NASA's Remote Agent on Deep Space One (Jonsson et al, 2000;Muscettola et al, 1998) does autonomous execution monitoring on a spacecraft. Our domain has many of the same requirements as NASA's.…”

Section: Monitoringmentioning

confidence: 99%

Airlift mission monitoring and dynamic rescheduling

Wilkins

Smith

Kramer

et al. 2008

Engineering Applications of Artificial Intelligence

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We describe the Flight Manager Assistant (FMA), a prototype system, designed to support real-time management of airlift operations at the USAF Air Mobility Command (AMC). In current practice, AMC flight managers are assigned to manage individual air missions. They tend to be overburdened with associated data monitoring and constraint checking, and generally react to detected problems in a local, myopic fashion. Consequently, decisions taken for one mission can often have deleterious effects on others. FMA combines two key capabilities for overcoming these problems: (1) intelligent monitoring of incoming information (for example, weather, airport operations, aircraft status) and recognition of those situations that require corrective action and (2) dynamic rescheduling of missions in response to detected problems, both to understand the global implications of changed circumstances and to determine appropriate rescheduling actions. FMA builds on two existing technologies: an execution-monitoring framework previously applied to small-unit operations and control of robots, and a dynamic scheduling tool that is transitioning into operational use in AMC's Tanker/Airlift Control Center. FMA's dynamic-mediation module provides for collaborative mission management by different planning and execution offices by structuring communication for decision making. r

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Remote Agent: to boldly go where no AI system has gone before

Cited by 442 publications

References 23 publications

Programming Groups of Rational Agents

Programming Groups of Rational Agents

Hybrid Planning and Scheduling

Airlift mission monitoring and dynamic rescheduling

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