R. J. Firby, Adaptive Execution in Complex Dynamic Domains. New Haven, CT: Yale University Technical Report YALEU/CSD/RR 672, January 1989.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....simulation when hardware resources are scarce, slow or expensive to operate. The objectives of our effort have many commonalities with other general purpose robot control systems such as the Task Control Architecture [1] 2] TCA) and the related systems TCX and IPC, the Reactive Action Package [3], 4] RAP) the RAVE architecture [5] or even the subsumption architecture [6] 7] A. Objectives The objectives of our archtecture are to provide robotics researchers with an abstract programming layer that provides access to a variety of robotic and sensing platforms. Included among the ....

R. J. Firby, Adaptive Execution in Complex Dynamic Domains. New Haven, CT: Yale University Technical Report YALEU/CSD/RR 672, January 1989.

....the agenda s content. In both cases, the top layer can add or delete items from the task agenda as well as reorganise or aggregate them. The top and middle layer therefore need to synchronise their access to the agenda. Middle tier: Context Driven Plan Re nement. Reactive Action Packages [16] are the basic blocks of a situation driven plan re nement system. A Reactive Action Package (RAP) groups together and describes all ways to carry out a speci c task in di erent situations. Fig. 3 displays (a simpli ed version of) the RAP instruct step. It has three possible situation speci c ....

Firby, R.J.: Adaptive Execution in Complex Dynamic Domains. PhD thesis, Yale University (1989) Technical Report YALEU/CSD/RR#672.

....common and unanticipated is to adopt reactive techniques. In these approaches, the selection and execution of individual tasks is closely integrated with the state of the environment, and changes in the environment are immediately reflected in the behavior of the agents within the system. Firby s [5] Reactive Action Packages (RAP) is one such system designed to handle agent execution in contexts in which agent actions might fail. Given a set of tasks for an agent to perform, Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without ....

....engine called the Mimesis Unreal Tournament Server (MUTS) The MUTS employs procedural representations of actions that mirror the declarative plan operators used by the MC s planning system. This parallel representation approach is similar to the relationship between task and method laid out in [5]. A more detailed description of the Mimesis architecture can be found in [21] 3.3 Detecting and Managing Unanticipated User Activity As described above, Mimesis drives the action within its story world based on the structure of a plan produced by a narrative planner. As users issue commands ....

Firby, R.J. (1994). Adaptive Execution in Complex Dynamic Domains. Ph.D. Thesis, Yale University Technical Report YALEU/CSD/RR #672.

....with his subsumption architecture [3] which relies on creating complex behavior through the interactions of simpler layers of control mechanisms. According to Brooks, instead of trying to model the world, mobile robots should just respond to it. A more recent approach, called the RAP system [16, 15], incorporates the compositional aspects of subsumption with the planning of the earlier systems. It uses modular reactive action packages (RAPs) as building blocks to construct a plan for reaction. The system carries out the actions in di#erent situations, despite most problems that may arise. ....

Jim Firby. Adaptive Execution in Complex Dynamic Domains. PhD Thesis, Yale University, January 1989.

.... points in a rover domain, where thinner edges are harder to traverse, and labeled points have associated observation goals While planning and scheduling efficiency is a major focus of our research, another is the support of flexible plan execution systems such as PRS [6] UMPRS [11] RAPS [5], JAM [7] etc. that exploit hierarchical plan spaces while interleaving task decomposition with execution. By postponing task decomposition, such systems gain flexibility to choose decompositions that best match current circumstances. However, this means that refinement decisions are made and ....

J. Firby. Adaptive Execution in Complex Dynamic Domains. PhD thesis, Yale Univ., 1989.

.... between points in a rover domain, where thinner edges are harder to traverse, and labeled points have associated observation goals While planning and scheduling efficiency is a major focus of our research, another is the support of flexible plan execution systems such as PRS [6] UMPRS [11] RAPS [5], JAM [7] etc. that exploit hierarchical plan spaces while interleaving task decomposition with execution. By postponing task decomposition, such systems gain flexibility to choose decompositions that best match current circumstances. However, this means that refinement decisions are made and ....

J. Firby. Adaptive Execution in Complex Dynamic Domains. PhD thesis, Yale Univ., 1989.

....of all of its potential refinements. Our motivation for doing this is not simply to make interleaved planning and merging with HTNs more efficient, but also to support another crucial use of HTN concepts specifically, flexible plan execution systems such as PRS (Georgeff Lansky 1986) RAPS (Firby 1989), etc. that similarly exploit hierarchical plan spaces. Rather than refine abstract plan operators into a detailed end to end plan, however, these systems interleave refinement with execution. By postponing refinement until absolutely necessary, such systems leave themselves flexibility to choose ....

Firby, J. 1989. Adaptive Execution in Complex Dynamic Domains. Ph.D. Dissertation, Yale University.

....of all of its potential refinements. Our motivation for doing this is not simply to make interleaved planning and merging with HTNs more efficient, but also to support another crucial use of HTN concepts specifically, flexible plan execution systems such as PRS (Georgeff Lansky 1986) RAPS (Firby 1989), etc. that similarly exploit hierarchical plan spaces. Rather than refine abstract plan operators into a detailed end to end plan, however, these systems interleave refinement with execution. By postponing refinement until absolutely necessary, such systems leave themselves flexibility to choose ....

Firby, J. 1989. Adaptive Execution in Complex Dynamic Domains. Ph.D. Dissertation, Yale University.

....information to show that this result also applies to the planning problem. Another advantage of finding abstract solutions is that the agent(s) retain flexibility in their plans by preserving further refinement choices. Robust plan execution systems such as PRS (Georgeff Lansky 1986) RAPS (Firby 1989), and JAM (Huber 1999) can then be used to interleave refinement with execution and provide some amount of recovery from unexpected events. Coordinating abstract plans involves reasoning about the concurrent execution of actions, but even for the planning problem, it is necessary to reason about ....

Firby, J. 1989. Adaptive Execution in Complex Dynamic Domains. Ph.D. Dissertation, Yale University.

....is always correct, but is not guaranteed to be complete, that is, no output might be generated. This framework is mainly intended to produce circuits that operate in real time, and some properties of their operation are provable. 3. 4 RAP The Reactive Action Packages (RAP) system as presented in [Fir89] proposes a plan and task representation based on program like reactive action packages, or RAPs. A plan consists of RAP de ned goals, or tasks, at a variety of di erent levels of abstraction. The RAP system executes the tasks in its task list depending on the satisfaction of the associated ....

R. James Firby. Adaptive Execution in Complex Dynamic Domains. PhD thesis, Yale University, 1989.

....task can be instantiated with either a quantitative or a qualitative path. So the instantiation of a move prototype depends on which planning prototype has been already instantiated. Although there has been a lot of work on task based run time robot configuration, a notable example being (Firby 1989), this experiment demonstrated that AUTOGNOSTIC s process, originally conceived for failure driven redesign, could also play the same role. 6.5 Interleaving Problem Solving and Redesign The last issue that arose directly from the reactive nature of a robot s behavior is the interaction between ....

FIRBY, J. (1989) Adaptive Execution in Complex Dynamic Domains. PhD Thesis, Tech. Report YALEU/CSD/RR #672 Yale University.

....and prefer rules. If several control rules are applicable in the current decision point, prodigy will use all of them. Select and reject rules are used to prune parts of the search space, while prefer rules determine in what order to explore the remaining parts. The rap reactive planner rap (Firby 1989), like other reactive planners, e.g. Georgeff Lansky 1986; McDermott 1990) is a system devoted to the execution of plans in a dynamic environment. Usually systems of this family are not able to generate plans by themselves but rather rely on a complex plan specification language to handwrite ....

Firby, R. J. 1989. Adaptive Execution in Complex Dynamic Domains. Ph.D. Dissertation, Yale University.

....by introducing synchronization actions [10] Just as agents can enhance planning efficiency by exploiting the hierarchical structure of planning operations, they can similarly enhance the efficiency and quality of coordinating their plans. Flexible plan execution systems such as PRS [11] RAPS [9], etc. interleave plan execution with the refinement of abstract plans into specific actions. By postponing refinement until absolutely necessary, such systems leave themselves flexibility to choose refinements that best match the current circumstances. However, when sharing resources with other ....

Firby, J. Adaptive Execution in Complex Dynamic Domains. Ph.D. Dissertation, Yale University, 1989.

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Firby, R. J. 1995. Adaptive Execution in Complex Dynamic Domains, Yale University Technical Report YALEU/CSD/RR #672.

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R. J. Firby, Adaptive Execution in Complex Dynamic Domains. New Haven, CT: Yale University Technical Report YALEU/CSD/RR 672, January 1989.

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