Compiling and Executing PDDL in Picat

1998

Abstract This manual describes the syntax of PDDL, the Planning Domain Definition Language, the problem-specification language for the AIPS-98 planning competition. The language has roughly the the expressiveness of Pednault's ADL [10] for propositions, and roughly the expressiveness of UMCP [6] for actions. Our hope is to encourage empirical evaluation of planner performance, and development of standard sets of problems all in comparable notations.

Abstract. Planning Domain Definition Language (PDDL) is the most used language to encode and solve planning problems. In this paper we propose two PDDL-like languages that extend PDDL with new constructs such as static causal laws and time fluents with the aim of improving the expressivity of PDDL language. We study the complexity of the main computational problems related to the planning problem in the new languages.

2005

The notion of plan quality in automated planning is a practically very important issue. In many real-world planning domains, we have to address problems with a large set of solutions, or with a set of goals that cannot all be achieved. In these problems, it is important to generate plans of good or optimal quality achieving all problem goals (if possible) or some subset of them.

Lecture Notes in Computer Science, 2015

We present a new declarative compilation of logic programs with constraints into variable-free relational theories which are then executed by rewriting. This translation provides an algebraic formulation of the abstract syntax of logic programs. Management of logic variables, unification, and renaming apart is completely elided in favor of algebraic manipulation of variable-free relation expressions. We prove the translation is sound, and the rewriting system complete with respect to traditional SLD semantics.

Lecture Notes in Computer Science, 2009

The marriage of model checking and planning faces two seemingly diverging alternatives: the need for a planning language expressive enough to capture the complexity of real-life applications, as opposed to a language simple, yet robust enough to be amenable to exhaustive verification and validation techniques. In an attempt to reconcile these differences, we have designed an abstract plan description language, ANMLite, inspired from the Action Notation Modeling Language (ANML). We present the basic concepts of the ANMLite language as well as an automatic translator from ANMLite to the model checker SAL (Symbolic Analysis Laboratory). We discuss various aspects of specifying a plan in terms of constraints and explore the implications of choosing a robust logic behind the specification of constraints, rather than simply propose a new planning language. Additionally, we provide an initial assessment of the efficiency of model checking to search for solutions of planning problems. To this end, we design a basic test benchmark and study the scalability of the generated SAL models in terms of plan complexity.

2020

The Planning Domain Definition Language (PDDL) is the state-of-the-art language for specifying planning problems in artificial intelligence research. Writing and maintaining these planning problems, however, can be time-consuming and error prone. To address this issue, we present myPDDL-a modular toolkit for developing and manipulating PDDL domains and problems. To evaluate myPDDL, we compare its features to existing knowledge engineering tools for PDDL. In a user test, we additionally assess two of its modules, namely the syntax highlighting feature and the type diagram generator. The users of syntax highlighting detected 36% more errors than non-users in an erroneous domain file. The average time on task for questions on a PDDL type hierarchy was reduced by 48% when making the type diagram generator available. This implies that myPDDL can support knowledge engineers well in the PDDL design and analysis process.

Journal of Artificial Intelligence Research, 2003

In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover exploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application.

2005

We discuss some features of the new logic programming language DALI for agents and multi-agent systems, also in connection to the issues raised in [12]. We focus in particular on the treatment of proactivity, which is based on the novel mechanism of the internal events and goals. As a case-study, we discuss the design and implementation of an agent capable to perform simple forms of planning. We demonstrate how it is possible in DALI to perform STRIPS-like planning without implementing a meta-interpreter.

Software: Practice and Experience, 2004

Logic programming requires that the programmer convert a problem into a set of constraints based on predicates. Choosing the predicates and introducing appropriate constraints can be intricate and error-prone. If the problem domain is structured enough, we can let the programmer express the problem in terms of more abstract, higherlevel constraints. A compiler can then convert the higher-level program into a logicprogramming formalism. The compiler writer can experiment with alternative lowlevel representations of the higher-level constraints in order to achieve a high-quality translation. The programmer can then take advantage of both a reduction in complexity and an improvement in runtime speed for all problems within the domain.

Lecture Notes in Computer Science, 1999

T OY is the concrete implementation of CRWL, a wide theoretical framework for declarative programming whose basis is a constructor based rewriting logic with lazy non-deterministic functions as the core notion. Other aspects of CRWL supported by T OY are: polymorphic types; HO features; equality and disequality constraints over terms and linear constraints over real numbers; goal solving by needed narrowing combined with constraint solving. The implementation is based on a compilation of T OY programs into Prolog.