Experiences with an architecture for intelligent, reactive agents

Bonasso, R. Peter; Kortenkamp, David; Miller, David P.; Slack, Marc G.

doi:10.1007/3540608052_67

Cited by 147 publications

(138 citation statements)

References 13 publications

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“…Focusing again on architectural issues, appearingly similar frameworks mostly deal with the connection of a deliberative high level planning component with reactive or rule-based execution monitoring and control modules [19,33]. This is especially the case for the integration of plan generation and plan execution.…”

Section: 3 Discussionmentioning

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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Section: 3 Discussionmentioning

confidence: 99%

Hybrid Planning and Scheduling

Schattenberg¹

2015

Künstl Intell

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“…• 3T (Bonasso et al, 1997) stores long-term knowledge in three layers or tiers. The lowest level consists of sensori-motor behaviors, which the architecture executes reactively, whereas the middle layer stores reactive action packages (Firby, 1994) that sequence these behaviors.…”

Section: Appendix Representative Cognitive Architecturesmentioning

confidence: 99%

Cognitive architectures: Research issues and challenges

Langley

Laird

Rogers

2009

Cognitive Systems Research

488

285

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“…Moreover, with the change in the goal of the robot, the configuration and modification in the architecture will also be changed. Robot must have the capability to take decision at runtime and adopt changes as and when required [13], [17], [18]. Fig.…”

Section: Design Considerationsmentioning

confidence: 99%

The Mobot Architecture: A Design of Autonomy Data Evaluation

Mansoor¹,

Hashmi²,

Khokhar³

2013

IJCEE

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Abstract-Most large, long term mobile robot systems start with some architecture. We studied some of the architectures which are implemented on robot previously, the advantages and disadvantages and finally select siemens 4 view architecture, the best possible architecture for mobile robot. In the designing phase of developing architecture of a robot using siemens 4 view, first of all we will identify the quality goals in the form of factor tables. Quality goals/Factor tables are basically broader statements which gave the information of the attributes, which this system will be capable to perform at the end and it will also provide different qualities that the final system should exhibit and are used to guide the development process. We will only identify some of the quality goals like navigation, security, monitoring, communication etc. Complete and detailed architecture will be developed in future. We named this architecture as Mobot.Index Terms-Quality goals, factor tables, issue card, siemens 4 view.

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Experiences with an architecture for intelligent, reactive agents

Cited by 147 publications

References 13 publications

Hybrid Planning and Scheduling

Hybrid Planning and Scheduling

Cognitive architectures: Research issues and challenges

The Mobot Architecture: A Design of Autonomy Data Evaluation

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