117,766 research outputs found
Inductive logic programming at 30
Inductive logic programming (ILP) is a form of logic-based machine learning.
The goal of ILP is to induce a hypothesis (a logic program) that generalises
given training examples and background knowledge. As ILP turns 30, we survey
recent work in the field. In this survey, we focus on (i) new meta-level search
methods, (ii) techniques for learning recursive programs that generalise from
few examples, (iii) new approaches for predicate invention, and (iv) the use of
different technologies, notably answer set programming and neural networks. We
conclude by discussing some of the current limitations of ILP and discuss
directions for future research.Comment: Extension of IJCAI20 survey paper. arXiv admin note: substantial text
overlap with arXiv:2002.11002, arXiv:2008.0791
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA
Strategic Directions in Object-Oriented Programming
This paper has provided an overview of the field of object-oriented programming. After presenting a historical perspective and some major achievements in the field, four research directions were introduced: technologies integration, software components, distributed programming, and new paradigms. In general there is a need to continue research in traditional areas:\ud
(1) as computer systems become more and more complex, there is a need to further develop the work on architecture and design; \ud
(2) to support the development of complex systems, there is a need for better languages, environments, and tools; \ud
(3) foundations in the form of the conceptual framework and other theories must be extended to enhance the means for modeling and formal analysis, as well as for understanding future computer systems
Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach
The definition of a concise and effective testbed for Genetic Programming
(GP) is a recurrent matter in the research community. This paper takes a new
step in this direction, proposing a different approach to measure the quality
of the symbolic regression benchmarks quantitatively. The proposed approach is
based on meta-learning and uses a set of dataset meta-features---such as the
number of examples or output skewness---to describe the datasets. Our idea is
to correlate these meta-features with the errors obtained by a GP method. These
meta-features define a space of benchmarks that should, ideally, have datasets
(points) covering different regions of the space. An initial analysis of 63
datasets showed that current benchmarks are concentrated in a small region of
this benchmark space. We also found out that number of instances and output
skewness are the most relevant meta-features to GP output error. Both
conclusions can help define which datasets should compose an effective testbed
for symbolic regression methods.Comment: 8 pages, 3 Figures, Proceedings of Genetic and Evolutionary
Computation Conference Companion, Kyoto, Japa
Meta-Level Inference and Program Verification
In [Bundy and Sterling 81] we described how meta-level inference was useful for controlling search and deriving control information in the domain of algebra. Similar techniques are applicable to the verification of logic programs. A developing meta-language is described, and an explicit proof plan using this language is given. A program, IMPRESS, is outlined which executes this plan
Instances and connectors : issues for a second generation process language
This work is supported by UK EPSRC grants GR/L34433 and GR/L32699Over the past decade a variety of process languages have been defined, used and evaluated. It is now possible to consider second generation languages based on this experience. Rather than develop a second generation wish list this position paper explores two issues: instances and connectors. Instances relate to the relationship between a process model as a description and the, possibly multiple, enacting instances which are created from it. Connectors refers to the issue of concurrency control and achieving a higher level of abstraction in how parts of a model interact. We believe that these issues are key to developing systems which can effectively support business processes, and that they have not received sufficient attention within the process modelling community. Through exploring these issues we also illustrate our approach to designing a second generation process language.Postprin
Instances and connectors : issues for a second generation process language
This work is supported by UK EPSRC grants GR/L34433 and GR/L32699Over the past decade a variety of process languages have been defined, used and evaluated. It is now possible to consider second generation languages based on this experience. Rather than develop a second generation wish list this position paper explores two issues: instances and connectors. Instances relate to the relationship between a process model as a description and the, possibly multiple, enacting instances which are created from it. Connectors refers to the issue of concurrency control and achieving a higher level of abstraction in how parts of a model interact. We believe that these issues are key to developing systems which can effectively support business processes, and that they have not received sufficient attention within the process modelling community. Through exploring these issues we also illustrate our approach to designing a second generation process language.Postprin
Assigning Satisfaction Values to Constraints: An Algorithm to Solve Dynamic Meta-Constraints
The model of Dynamic Meta-Constraints has special activity constraints which
can activate other constraints. It also has meta-constraints which range over
other constraints. An algorithm is presented in which constraints can be
assigned one of five different satisfaction values, which leads to the
assignment of domain values to the variables in the CSP. An outline of the
model and the algorithm is presented, followed by some initial results for two
problems: a simple classic CSP and the Car Configuration Problem. The algorithm
is shown to perform few backtracks per solution, but to have overheads in the
form of historical records required for the implementation of state.Comment: 11 pages. Proceedings ERCIM WG on Constraints (Prague, June 2001
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