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.

scholarly journals HDDL: An Extension to PDDL for Expressing Hierarchical Planning Problems

2020 ◽  
Vol 34 (06) ◽  
pp. 9883-9891 ◽  
Author(s):  
Daniel Höller ◽  
Gregor Behnke ◽  
Pascal Bercher ◽  
Susanne Biundo ◽  
Humbert Fiorino ◽  
...  
Keyword(s):  
Planning System ◽  
Hierarchical Planning ◽  
Planning Systems ◽  
Description Language ◽  
Input Language ◽  
Common Input ◽  
Modeling Process ◽  
Planning Problems ◽  
Domain Independent ◽  
Tailored Advice

The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with sophisticated solving techniques. In principle, this development would make the comparison between systems easier (because the domains are not tailored to a single system anymore) and – much more important – also the integration into other systems, because the modeling process is less tedious (due to the lack of advice) and there is no (or less) commitment to a certain planning system the model is created for. However, these advantages are destroyed by the lack of a common input language and feature set supported by the different systems. In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems.

scholarly journals Integrating State Constraints and Obligations in Situation Calculus

2009 ◽  
Vol 13 (41) ◽  
Author(s):  
Robert Demolombe ◽  
Pilar Pozos Parra
Keyword(s):  
State Constraints ◽  
Situation Calculus

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.

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 Extending Classical Planning with State Constraints: Heuristics and Search for Optimal Planning

2018 ◽  
Vol 62 ◽  
pp. 373-431 ◽  
Author(s):  
Patrik Haslum ◽  
Franc Ivankovic ◽  
Miquel Ramirez ◽  
Dan Gordon ◽  
Sylvie Thiebaux ◽  
...  
Keyword(s):  
State Constraints ◽  
Linear Equations ◽  
Discrete Control ◽  
Optimal Planning ◽  
Ai Planning ◽  
State Variables ◽  
Power Networks ◽  
Admissible Heuristics ◽  
Classical Setting ◽  
Continuous Quantities

We present a principled way of extending a classical AI planning formalism with systems of state constraints, which relate - sometimes determine - the values of variables in each state traversed by the plan. This extension occupies an attractive middle ground between expressivity and complexity. It enables modelling a new range of problems, as well as formulating more efficient models of classical planning problems. An example of the former is planning-based control of networked physical systems - power networks, for example - in which a local, discrete control action can have global effects on continuous quantities, such as altering flows across the entire network. At the same time, our extension remains decidable as long as the satisfiability of sets of state constraints is decidable, including in the presence of numeric state variables, and we demonstrate that effective techniques for cost-optimal planning known in the classical setting - in particular, relaxation-based admissible heuristics - can be adapted to the extended formalism. In this paper, we apply our approach to constraints in the form of linear or non-linear equations over numeric state variables, but the approach is independent of the type of state constraints, as long as there exists a procedure that decides their consistency. The planner and the constraint solver interact through a well-defined, narrow interface, in which the solver requires no specialisation to the planning context.

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.

Changing Beliefs about Domain Dynamics in the Situation Calculus

2020 ◽  
Author(s):  
Toryn Q. Klassen ◽  
Sheila A. McIlraith ◽  
Hector J. Levesque
Keyword(s):  
Belief Revision ◽  
Belief Change ◽  
Situation Calculus ◽  
Action Effects ◽  
The World ◽  
Domain Dynamics

Agents change their beliefs about the plausibility of various aspects of domain dynamics -- effects of physical actions, results of sensing, and action preconditions -- as a consequence of their interactions with the world. In this paper we propose a way to conveniently represent domain dynamics in the situation calculus to support such belief change. Furthermore, we suggest patterns to follow when writing the axioms that describe the effects of actions, and prove how these patterns can control the extent to which observations change the agent's beliefs about action effects. We also discuss the relation of our work to the AGM postulates for belief revision. Finally, we show how beliefs about domain dynamics can be incorporated into a form of regression rewriting to support reasoning.

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