53,810 research outputs found
2Planning for Contingencies: A Decision-based Approach
A fundamental assumption made by classical AI planners is that there is no
uncertainty in the world: the planner has full knowledge of the conditions
under which the plan will be executed and the outcome of every action is fully
predictable. These planners cannot therefore construct contingency plans, i.e.,
plans in which different actions are performed in different circumstances. In
this paper we discuss some issues that arise in the representation and
construction of contingency plans and describe Cassandra, a partial-order
contingency planner. Cassandra uses explicit decision-steps that enable the
agent executing the plan to decide which plan branch to follow. The
decision-steps in a plan result in subgoals to acquire knowledge, which are
planned for in the same way as any other subgoals. Cassandra thus distinguishes
the process of gathering information from the process of making decisions. The
explicit representation of decisions in Cassandra allows a coherent approach to
the problems of contingent planning, and provides a solid base for extensions
such as the use of different decision-making procedures.Comment: See http://www.jair.org/ for any accompanying file
Design project planning, monitoring and re-planning through process simulation
Effective management of design schedules is a major concern in industry, since timely project delivery can have a significant influence on a companyās profitability. Based on insights gained through a case study of planning practice in aero-engine component design, this paper examines how task network simulation models can be deployed in a new way to support design process planning. Our method shows how simulation can be used to reconcile a description of design activities and information flows with project targets such as milestone delivery dates. It also shows how monitoring and re-planning can be supported using the non-ideal metrics which the case study revealed are used to monitor processes in practice. The approach is presented as a theoretical contribution which requires further work to implement and evaluate in practice
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Repeatable approaches to work with scientific uncertainty and advance climate change adaptation in US national parks
Progress in AI Planning Research and Applications
Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning
Planning Graph Heuristics for Belief Space Search
Some recent works in conditional planning have proposed reachability
heuristics to improve planner scalability, but many lack a formal description
of the properties of their distance estimates. To place previous work in
context and extend work on heuristics for conditional planning, we provide a
formal basis for distance estimates between belief states. We give a definition
for the distance between belief states that relies on aggregating underlying
state distance measures. We give several techniques to aggregate state
distances and their associated properties. Many existing heuristics exhibit a
subset of the properties, but in order to provide a standardized comparison we
present several generalizations of planning graph heuristics that are used in a
single planner. We compliment our belief state distance estimate framework by
also investigating efficient planning graph data structures that incorporate
BDDs to compute the most effective heuristics.
We developed two planners to serve as test-beds for our investigation. The
first, CAltAlt, is a conformant regression planner that uses A* search. The
second, POND, is a conditional progression planner that uses AO* search. We
show the relative effectiveness of our heuristic techniques within these
planners. We also compare the performance of these planners with several state
of the art approaches in conditional planning
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