151,807 research outputs found
POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem
Most of computer science focuses on automatically solving given computational
problems. I focus on automatically inventing or discovering problems in a way
inspired by the playful behavior of animals and humans, to train a more and
more general problem solver from scratch in an unsupervised fashion. Consider
the infinite set of all computable descriptions of tasks with possibly
computable solutions. The novel algorithmic framework POWERPLAY (2011)
continually searches the space of possible pairs of new tasks and modifications
of the current problem solver, until it finds a more powerful problem solver
that provably solves all previously learned tasks plus the new one, while the
unmodified predecessor does not. Wow-effects are achieved by continually making
previously learned skills more efficient such that they require less time and
space. New skills may (partially) re-use previously learned skills. POWERPLAY's
search orders candidate pairs of tasks and solver modifications by their
conditional computational (time & space) complexity, given the stored
experience so far. The new task and its corresponding task-solving skill are
those first found and validated. The computational costs of validating new
tasks need not grow with task repertoire size. POWERPLAY's ongoing search for
novelty keeps breaking the generalization abilities of its present solver. This
is related to Goedel's sequence of increasingly powerful formal theories based
on adding formerly unprovable statements to the axioms without affecting
previously provable theorems. The continually increasing repertoire of problem
solving procedures can be exploited by a parallel search for solutions to
additional externally posed tasks. POWERPLAY may be viewed as a greedy but
practical implementation of basic principles of creativity. A first
experimental analysis can be found in separate papers [53,54].Comment: 21 pages, additional connections to previous work, references to
first experiments with POWERPLA
Towards Closed World Reasoning in Dynamic Open Worlds (Extended Version)
The need for integration of ontologies with nonmonotonic rules has been
gaining importance in a number of areas, such as the Semantic Web. A number of
researchers addressed this problem by proposing a unified semantics for hybrid
knowledge bases composed of both an ontology (expressed in a fragment of
first-order logic) and nonmonotonic rules. These semantics have matured over
the years, but only provide solutions for the static case when knowledge does
not need to evolve. In this paper we take a first step towards addressing the
dynamics of hybrid knowledge bases. We focus on knowledge updates and,
considering the state of the art of belief update, ontology update and rule
update, we show that current solutions are only partial and difficult to
combine. Then we extend the existing work on ABox updates with rules, provide a
semantics for such evolving hybrid knowledge bases and study its basic
properties. To the best of our knowledge, this is the first time that an update
operator is proposed for hybrid knowledge bases.Comment: 40 pages; an extended version of the article published in Theory and
Practice of Logic Programming, 10 (4-6): 547 - 564, July. Copyright 2010
Cambridge University Pres
Time Series Data Mining: A Retail Application Using SAS Enterprise Miner
Modern technologies have allowed for the amassment of data at a rate never encountered before. Organizations are now able to routinely collect and process massive volumes of data. A plethora of regularly collected information can be ordered using an appropriate time interval. The data would thus be developed into a time series. With such data, analytical techniques can be employed to collect information pertaining to historical trends and seasonality. Time series data mining methodology allows users to identify commonalities between sets of time-ordered data. This technique is supported by a variety of algorithms, notably dynamic time warping (DTW). This mathematical technique supports the identification of similarities between numerous time series. The following research aims to provide a practical application of this methodology using SAS Enterprise Miner, an industry-leading software platform for business analytics. Due to the prevalence of time series data in retail settings, a realistic product sales transaction data set was analyzed. This information was provided by dunnhumbyUSA. Interpretations were drawn from output that was generated using “TS nodes” in SAS Enterprise Miner
Towards the implementation of a preference-and uncertain-aware solver using answer set programming
Logic programs with possibilistic ordered disjunction (or LPPODs) are a recently defined logic-programming framework based on logic programs with ordered disjunction and possibilistic logic. The framework inherits the properties of such formalisms and merging them, it supports a reasoning which is nonmonotonic, preference-and uncertain-aware. The LPPODs syntax allows to specify 1) preferences in a qualitative way, and 2) necessity values about the certainty of program clauses. As a result at semantic level, preferences and necessity values can be used to specify an order among program solutions. This class of program therefore fits well in the representation of decision problems where a best option has to be chosen taking into account both preferences and necessity measures about information. In this paper we study the computation and the complexity of the LPPODs semantics and we describe the algorithm for its implementation following on Answer Set Programming approach. We describe some decision scenarios where the solver can be used to choose the best solutions by checking whether an outcome is possibilistically preferred over another considering preferences and uncertainty at the same time.Postprint (published version
A Linear Logic Programming Language for Concurrent Programming over Graph Structures
We have designed a new logic programming language called LM (Linear Meld) for
programming graph-based algorithms in a declarative fashion. Our language is
based on linear logic, an expressive logical system where logical facts can be
consumed. Because LM integrates both classical and linear logic, LM tends to be
more expressive than other logic programming languages. LM programs are
naturally concurrent because facts are partitioned by nodes of a graph data
structure. Computation is performed at the node level while communication
happens between connected nodes. In this paper, we present the syntax and
operational semantics of our language and illustrate its use through a number
of examples.Comment: ICLP 2014, TPLP 201
Tabling with Sound Answer Subsumption
Tabling is a powerful resolution mechanism for logic programs that captures
their least fixed point semantics more faithfully than plain Prolog. In many
tabling applications, we are not interested in the set of all answers to a
goal, but only require an aggregation of those answers. Several works have
studied efficient techniques, such as lattice-based answer subsumption and
mode-directed tabling, to do so for various forms of aggregation.
While much attention has been paid to expressivity and efficient
implementation of the different approaches, soundness has not been considered.
This paper shows that the different implementations indeed fail to produce
least fixed points for some programs. As a remedy, we provide a formal
framework that generalises the existing approaches and we establish a soundness
criterion that explains for which programs the approach is sound.
This article is under consideration for acceptance in TPLP.Comment: Paper presented at the 32nd International Conference on Logic
Programming (ICLP 2016), New York City, USA, 16-21 October 2016, 15 pages,
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