3,748 research outputs found
ACL2(ml):machine-learning for ACL2
ACL2(ml) is an extension for the Emacs interface of ACL2. This tool uses
machine-learning to help the ACL2 user during the proof-development. Namely,
ACL2(ml) gives hints to the user in the form of families of similar theorems,
and generates auxiliary lemmas automatically. In this paper, we present the two
most recent extensions for ACL2(ml). First, ACL2(ml) can suggest now families
of similar function definitions, in addition to the families of similar
theorems. Second, the lemma generation tool implemented in ACL2(ml) has been
improved with a method to generate preconditions using the guard mechanism of
ACL2. The user of ACL2(ml) can also invoke directly the latter extension to
obtain preconditions for his own conjectures.Comment: In Proceedings ACL2 2014, arXiv:1406.123
Building and Refining Abstract Planning Cases by Change of Representation Language
ion is one of the most promising approaches to improve the performance of
problem solvers. In several domains abstraction by dropping sentences of a
domain description -- as used in most hierarchical planners -- has proven
useful. In this paper we present examples which illustrate significant
drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we
propose a more general view of abstraction involving the change of
representation language. We have developed a new abstraction methodology and a
related sound and complete learning algorithm that allows the complete change
of representation language of planning cases from concrete to abstract.
However, to achieve a powerful change of the representation language, the
abstract language itself as well as rules which describe admissible ways of
abstracting states must be provided in the domain model. This new abstraction
approach is the core of Paris (Plan Abstraction and Refinement in an Integrated
System), a system in which abstract planning cases are automatically learned
from given concrete cases. An empirical study in the domain of process planning
in mechanical engineering shows significant advantages of the proposed
reasoning from abstract cases over classical hierarchical planning.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
Get my pizza right: Repairing missing is-a relations in ALC ontologies (extended version)
With the increased use of ontologies in semantically-enabled applications,
the issue of debugging defects in ontologies has become increasingly important.
These defects can lead to wrong or incomplete results for the applications.
Debugging consists of the phases of detection and repairing. In this paper we
focus on the repairing phase of a particular kind of defects, i.e. the missing
relations in the is-a hierarchy. Previous work has dealt with the case of
taxonomies. In this work we extend the scope to deal with ALC ontologies that
can be represented using acyclic terminologies. We present algorithms and
discuss a system
Proof-Pattern Recognition and Lemma Discovery in ACL2
We present a novel technique for combining statistical machine learning for
proof-pattern recognition with symbolic methods for lemma discovery. The
resulting tool, ACL2(ml), gathers proof statistics and uses statistical
pattern-recognition to pre-processes data from libraries, and then suggests
auxiliary lemmas in new proofs by analogy with already seen examples. This
paper presents the implementation of ACL2(ml) alongside theoretical
descriptions of the proof-pattern recognition and lemma discovery methods
involved in it
Derivation of sorting programs
Program synthesis for critical applications has become a viable alternative to program verification. Nested resolution and its extension are used to synthesize a set of sorting programs from their first order logic specifications. A set of sorting programs, such as, naive sort, merge sort, and insertion sort, were successfully synthesized starting from the same set of specifications
Applying semantic web technologies to knowledge sharing in aerospace engineering
This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale
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