14,550 research outputs found
A Domain-Independent Algorithm for Plan Adaptation
The paradigms of transformational planning, case-based planning, and plan
debugging all involve a process known as plan adaptation - modifying or
repairing an old plan so it solves a new problem. In this paper we provide a
domain-independent algorithm for plan adaptation, demonstrate that it is sound,
complete, and systematic, and compare it to other adaptation algorithms in the
literature. Our approach is based on a view of planning as searching a graph of
partial plans. Generative planning starts at the graph's root and moves from
node to node using plan-refinement operators. In planning by adaptation, a
library plan - an arbitrary node in the plan graph - is the starting point for
the search, and the plan-adaptation algorithm can apply both the same
refinement operators available to a generative planner and can also retract
constraints and steps from the plan. Our algorithm's completeness ensures that
the adaptation algorithm will eventually search the entire graph and its
systematicity ensures that it will do so without redundantly searching any
parts of the graph.Comment: See http://www.jair.org/ for any accompanying file
SHOP2: An HTN Planning System
The SHOP2 planning system received one of the awards for distinguished
performance in the 2002 International Planning Competition. This paper
describes the features of SHOP2 which enabled it to excel in the competition,
especially those aspects of SHOP2 that deal with temporal and metric planning
domains
The 2014 International Planning Competition: Progress and Trends
We review the 2014 International Planning Competition (IPC-2014), the eighth
in a series of competitions starting in 1998. IPC-2014 was held in three separate
parts to assess state-of-the-art in three prominent areas of planning research: the
deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic
part (IPPC). Each part evaluated planning systems in ways that pushed the edge of
existing planner performance by introducing new challenges, novel tasks, or both.
The competition surpassed again the number of competitors than its predecessor,
highlighting the competition’s central role in shaping the landscape of ongoing
developments in evaluating planning systems
Multi Agent Systems in Logistics: A Literature and State-of-the-art Review
Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems
A Survey of Monte Carlo Tree Search Methods
Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work
Zero-gravity movement studies
The use of computer graphics to simulate the movement of articulated animals and mechanisms has a number of uses ranging over many fields. Human motion simulation systems can be useful in education, medicine, anatomy, physiology, and dance. In biomechanics, computer displays help to understand and analyze performance. Simulations can be used to help understand the effect of external or internal forces. Similarly, zero-gravity simulation systems should provide a means of designing and exploring the capabilities of hypothetical zero-gravity situations before actually carrying out such actions. The advantage of using a simulation of the motion is that one can experiment with variations of a maneuver before attempting to teach it to an individual. The zero-gravity motion simulation problem can be divided into two broad areas: human movement and behavior in zero-gravity, and simulation of articulated mechanisms
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Planning in constraint space for multi-body manipulation tasks
Robots are inherently limited by physical constraints on their link lengths, motor torques, battery
power and structural rigidity. To thrive in circumstances that push these limits, such as in search
and rescue scenarios, intelligent agents can use the available objects in their environment as
tools. Reasoning about arbitrary objects and how they can be placed together to create useful
structures such as ramps, bridges or simple machines is critical to push beyond one's physical
limitations. Unfortunately, the solution space is combinatorial in the number of available objects
and the configuration space of the chosen objects and the robot that uses the structure is high
dimensional.
To address these challenges, we propose using constraint satisfaction as a means to test the
feasibility of candidate structures and adopt search algorithms in the classical planning literature
to find sufficient designs. The key idea is that the interactions between the components of a
structure can be encoded as equality and inequality constraints on the configuration spaces of the
respective objects. Furthermore, constraints that are induced by a broadly defined action, such as
placing an object on another, can be grouped together using logical representations such as Planning
Domain Definition Language (PDDL). Then, a classical planning search algorithm can reason about
which set of constraints to impose on the available objects, iteratively creating a structure that
satisfies the task goals and the robot constraints. To demonstrate the effectiveness of this
framework, we present both simulation and real robot results with static structures such as ramps,
bridges and stairs, and quasi-static structures such as lever-fulcrum simple machines.Ph.D
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