Organization of the 1st International Conference on AI Planning Systems

1992 ◽  
pp. iii
Keyword(s):  
Ai Planning ◽  
Planning Systems ◽  
International Conference

PLANNING AND REACTING ACROSS SUPERVENIENT LEVELS OF REPRESENTATION

1992 ◽  
Vol 01 (03n04) ◽  
pp. 411-449 ◽  
Author(s):  
LEE SPECTOR ◽  
JAMES HENDLER
Keyword(s):  
Control Systems ◽  
Intelligent Systems ◽  
Hierarchical Control ◽  
Dynamic Environments ◽  
Ai Planning ◽  
Planning Systems ◽  
Complex Dynamic ◽  
Strong Constraint ◽  
External Agents

For intelligent systems to interact with external agents and changing domains, they must be able to perceive and to affect their environments while computing long term projection (planning) of future states. This paper describes and demonstrates the supervenience architecture, a multilevel architecture for integrating planning and reacting in complex, dynamic environments. We briefly review the underlying concept of supervenience, a form of abstraction with affinities both to abstraction in AI planning systems, and to knowledge-partitioning schemes in hierarchical control systems. We show how this concept can be distilled into a strong constraint on the design of dynamic-world planning systems. We then describe the supervenience architecture and an implementation of the architecture called APE (for Abstraction-Partitioned Evaluator). The application of APE to the HomeBot domain is used to demonstrate the capabilities of the architecture.

scholarly journals Compiling Causal Theories to Successor State Axioms and STRIPS-Like Systems

2003 ◽  
Vol 19 ◽  
pp. 279-314 ◽  
Author(s):  
F. Lin
Keyword(s):  
State Constraints ◽  
Causal Theory ◽  
Situation Calculus ◽  
Formal Specifications ◽  
Ai Planning ◽  
Planning Systems ◽  
Description Language ◽  
Successor State ◽  
Action Effects ◽  
Causal Theories

We describe a system for specifying the effects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify the effects of actions using domain rules, which are state constraints that can entail new action effects from old ones. Declaratively, an action domain in our language corresponds to a nonmonotonic causal theory in the situation calculus. Procedurally, such an action domain is compiled into a set of logical theories, one for each action in the domain, from which fully instantiated successor state-like axioms and STRIPS-like systems are then generated. We expect the system to be a useful tool for knowledge engineers writing action specifications for classical AI planning systems, GOLOG systems, and other systems where formal specifications of actions are needed.

A MOTIVATION-BASED PLANNING AND EXECUTION FRAMEWORK

2004 ◽  
Vol 13 (01) ◽  
pp. 5-25 ◽  
Author(s):  
ALEXANDRA M. CODDINGTON ◽  
MICHAEL LUCK
Keyword(s):  
Ai Planning ◽  
Planning Systems ◽  
Planning Framework ◽  
Plan Selection ◽  
Domain Independent

AI planning systems tend to be disembodied and are not situated within the environment for which plans are generated, thus losing information concerning the interaction between the system and its environment. This paper argues that such information may potentially be valuable in constraining plan formulation, and presents both an agent- and domain-independent architecture that extends the classical AI planning framework to take into account context, or the interaction between an autonomous situated planning agent and its environment. The paper describes how context constrains the goals an agent might generate, enables those goals to be prioritised, and constrains plan selection.

scholarly journals Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning

2020 ◽  
Author(s):  
Dieqiao Feng ◽  
Carla Gomes ◽  
Bart Selman
Keyword(s):  
Reinforcement Learning ◽  
Training Curriculum ◽  
Ai Planning ◽  
Significant Progress ◽  
Planning Systems ◽  
Domain Specific ◽  
Proper Training ◽  
Planning Task ◽  
Planning Problems ◽  
Pruning Techniques

Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems. Sokoban is a PSPACE-complete planning task and represents one of the hardest domains for current AI planners. Even domain-specific specialized search methods fail quickly due to the exponential search complexity on hard instances. Our approach based on deep reinforcement learning augmented with a curriculum-driven method is the first one to solve hard instances within one day of training while other modern solvers cannot solve these instances within any reasonable time limit. In contrast to prior efforts, which use carefully handcrafted pruning techniques, our approach automatically uncovers domain structure. Our results reveal that deep RL provides a promising framework for solving previously unsolved AI planning problems, provided a proper training curriculum can be devised.

scholarly journals The 3rd International Planning Competition: Results and Analysis

2003 ◽  
Vol 20 ◽  
pp. 1-59 ◽  
Author(s):  
D. Long ◽  
M. Fox
Keyword(s):  
Statistical Analysis ◽  
Ai Planning ◽  
Relative Difficulty ◽  
Comparative Performance ◽  
Planning And Scheduling ◽  
International Conference ◽  
The Third ◽  
The Future ◽  
Problem Instances ◽  
International Planning

This paper reports the outcome of the third in the series of biennial international planning competitions, held in association with the International Conference on AI Planning and Scheduling (AIPS) in 2002. In addition to describing the domains, the planners and the objectives of the competition, the paper includes analysis of the results. The results are analysed from several perspectives, in order to address the questions of comparative performance between planners, comparative difficulty of domains, the degree of agreement between planners about the relative difficulty of individual problem instances and the question of how well planners scale relative to one another over increasingly difficult problems. The paper addresses these questions through statistical analysis of the raw results of the competition, in order to determine which results can be considered to be adequately supported by the data. The paper concludes with a discussion of some challenges for the future of the competition series.

Activity aggregation in model-based AI planning systems

1993 ◽  
Vol 7 (3) ◽  
pp. 209-228
Author(s):  
Graham Winstanley ◽  
Kunito Hoshi
Keyword(s):  
Integrated Design ◽  
Specific Model ◽  
Project Planning ◽  
Planning System ◽  
Ai Planning ◽  
Planning Systems ◽  
Model Based ◽  
Flexible Planning ◽  
Industrial Projects ◽  
Engineered Systems

When model-based planning systems are scaled up to deal with full-sized industrial projects, the resulting complexity in the project-specific model and production plan can create serious problems, not only in dealing with such complexity computationally, but also in user-acceptance. In the model-based planning system described in this paper, activities are dynamically generated, inherently at the detailed level of individual physical components. However, it is possible to intelligently group together collections of components which would be common to realistic work packages, and hence schedule on the basis of virtual components existing within an abstraction hierarchy. This paper describes a technique of project planning within an integrated design/planning system, which exploits fundamental knowledge of engineered systems and provides powerful and flexible planning functionality.

Post-design analysis for building and refining AI planning systems

2013 ◽  
Vol 26 (8) ◽  
pp. 1967-1979 ◽  
Author(s):  
Tiago Stegun Vaquero ◽  
José Reinaldo Silva ◽  
J. Christopher Beck
Keyword(s):  
Design Analysis ◽  
Ai Planning ◽  
Planning Systems

A Review of knowledge-based planning techniques

1984 ◽  
Vol 1 (2) ◽  
pp. 4-17 ◽  
Author(s):  
Austin Tate
Keyword(s):  
Historical Development ◽  
Research Topic ◽  
Ai Planning ◽  
Important Research ◽  
Planning Systems ◽  
Planning Techniques ◽  
Knowledge Based ◽  
Research Themes ◽  
Knowledge Based Planning ◽  
Active Research

SummaryPlanning systems have been an active research topic within Artificial Intelligence for over two decades. There have been a number of techniques developed during that period which still form an essential part of many of today's planners. This paper introduces the techniques, attempts to classify some of the important research themes in AI planning and describes their historical development.

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