26 research outputs found
Planning as Tabled Logic Programming
This paper describes Picat's planner, its implementation, and planning models
for several domains used in International Planning Competition (IPC) 2014.
Picat's planner is implemented by use of tabling. During search, every state
encountered is tabled, and tabled states are used to effectively perform
resource-bounded search. In Picat, structured data can be used to avoid
enumerating all possible permutations of objects, and term sharing is used to
avoid duplication of common state data. This paper presents several modeling
techniques through the example models, ranging from designing state
representations to facilitate data sharing and symmetry breaking, encoding
actions with operations for efficient precondition checking and state updating,
to incorporating domain knowledge and heuristics. Broadly, this paper
demonstrates the effectiveness of tabled logic programming for planning, and
argues the importance of modeling despite recent significant progress in
domain-independent PDDL planners.Comment: 27 pages in TPLP 201
A Logic Programming Approach to Knowledge-State Planning: Semantics and Complexity
We propose a new declarative planning language, called K, which is based on
principles and methods of logic programming. In this language, transitions
between states of knowledge can be described, rather than transitions between
completely described states of the world, which makes the language well-suited
for planning under incomplete knowledge. Furthermore, it enables the use of
default principles in the planning process by supporting negation as failure.
Nonetheless, K also supports the representation of transitions between states
of the world (i.e., states of complete knowledge) as a special case, which
shows that the language is very flexible. As we demonstrate on particular
examples, the use of knowledge states may allow for a natural and compact
problem representation. We then provide a thorough analysis of the
computational complexity of K, and consider different planning problems,
including standard planning and secure planning (also known as conformant
planning) problems. We show that these problems have different complexities
under various restrictions, ranging from NP to NEXPTIME in the propositional
case. Our results form the theoretical basis for the DLV^K system, which
implements the language K on top of the DLV logic programming system.Comment: 48 pages, appeared as a Technical Report at KBS of the Vienna
University of Technology, see http://www.kr.tuwien.ac.at/research/reports
Parsing Combinatory Categorial Grammar with Answer Set Programming: Preliminary Report
Combinatory categorial grammar (CCG) is a grammar formalism used for natural
language parsing. CCG assigns structured lexical categories to words and uses a
small set of combinatory rules to combine these categories to parse a sentence.
In this work we propose and implement a new approach to CCG parsing that relies
on a prominent knowledge representation formalism, answer set programming (ASP)
- a declarative programming paradigm. We formulate the task of CCG parsing as a
planning problem and use an ASP computational tool to compute solutions that
correspond to valid parses. Compared to other approaches, there is no need to
implement a specific parsing algorithm using such a declarative method. Our
approach aims at producing all semantically distinct parse trees for a given
sentence. From this goal, normalization and efficiency issues arise, and we
deal with them by combining and extending existing strategies. We have
implemented a CCG parsing tool kit - AspCcgTk - that uses ASP as its main
computational means. The C&C supertagger can be used as a preprocessor within
AspCcgTk, which allows us to achieve wide-coverage natural language parsing.Comment: 12 pages, 2 figures, Proceedings of the 25th Workshop on Logic
Programming (WLP 2011
Route planning algorithms: Planific@ Project
Planific@ is a route planning project for the city of
Madrid (Spain). Its main aim is to develop an intelligence system
capable of routing people from one place in the city to any other
using the public transport. In order to do this, it is necessary to
take into account such things as: time, traffic, user preferences,
etc. Before beginning to design the project is necessary to make
a comprehensive study of the variety of main known route
planning algorithms suitable to be used in this projec
\u3ci\u3eCorrect Reasoning: Essays on Logic-Based AI in Honour of Vladimir Lifschitz\u3c/i\u3e
Co-edited by Yuliya Lierler, UNO faculty member.
Essay, Parsing Combinatory Categorial Grammar via Planning in Answer Set Programming, co-authored by Yuliya Lierler, UNO faculty member.
This Festschrift published in honor of Vladimir Lifschitz on the occasion of his 65th birthday presents 39 articles by colleagues from all over the world with whom Vladimir Lifschitz had cooperation in various respects. The 39 contributions reflect the breadth and the depth of the work of Vladimir Lifschitz in logic programming, circumscription, default logic, action theory, causal reasoning and answer set programming.https://digitalcommons.unomaha.edu/facultybooks/1231/thumbnail.jp
Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs
We establish a novel relation between delete-free planning, an important task
for the AI Planning community also known as relaxed planning, and logic
programming. We show that given a planning problem, all subsets of actions that
could be ordered to produce relaxed plans for the problem can be bijectively
captured with stable models of a logic program describing the corresponding
relaxed planning problem. We also consider the supported model semantics of
logic programs, and introduce one causal and one diagnostic encoding of the
relaxed planning problem as logic programs, both capturing relaxed plans with
their supported models. Our experimental results show that these new encodings
can provide major performance gain when computing optimal relaxed plans, with
our diagnostic encoding outperforming state-of-the-art approaches to relaxed
planning regardless of the given time limit when measured on a wide collection
of STRIPS planning benchmarks.Comment: Paper presented at the 39th International Conference on Logic
Programming (ICLP 2023), 14 page
Characterizing and Computing All Delete-Relaxed Dead-ends
Dead-end detection is a key challenge in automated planning, and it is rapidly growing in popularity. Effective dead-end detection techniques can have a large impact on the strength of a planner, and so the effective computation of dead-ends is central to many planning approaches. One of the better understood techniques for detecting dead-ends is to focus on the delete relaxation of a planning problem, where dead-end detection is a polynomial-time operation. In this work, we provide a logical characterization for not just a single dead-end, but for every delete-relaxed dead-end in a planning problem. With a logical representation in hand, one could compile the representation into a form amenable to effective reasoning. We lay the ground-work for this larger vision and provide a preliminary evaluation to this en