Answer Set programming (ASP) is a well-known symbolic AI formalism developed in the area of knowledge representation and reasoning. This paper reports on some blendings of ASP with neural approaches, that ca...
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MagicHaskeller is our inductive functional programming library based on systematic search. In this paper we introduce two recent improvements to MagicHaskeller, i.e. 1) clarification and extension to arbitrary-rank po...
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ISBN:
(纸本)9783642119309
MagicHaskeller is our inductive functional programming library based on systematic search. In this paper we introduce two recent improvements to MagicHaskeller, i.e. 1) clarification and extension to arbitrary-rank polymorphism of its algorithm, and 2) efficiency improvement in its filtration algorithm that removes redundancy in the search results.
inductiveprogramming (IP)-the use of inductive reasoning methods for programming, algorithm design, and software development is a currently emerging research field. A major subfield is inductive program synthesis, th...
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ISBN:
(纸本)9783642119309
inductiveprogramming (IP)-the use of inductive reasoning methods for programming, algorithm design, and software development is a currently emerging research field. A major subfield is inductive program synthesis, the (semi-)automatic construction of programs from exemplary behavior. inductive program synthesis is not a unified research field until today but scattered over several different established research fields such as machine learning, inductive logic programming, genetic programming, and functional programming. This impedes an exchange of theory and techniques and, as a consequence, a progress of inductiveprogramming. In this paper we survey theoretical results and methods of inductive program synthesis that have been developed in different research fields until today.
This paper describes a very flexible way to synthesize functions matching a given predicate. This can be used to find general recursive functions or A-terms obeying an input output behavior specified by a number of ex...
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ISBN:
(纸本)9783642119309
This paper describes a very flexible way to synthesize functions matching a given predicate. This can be used to find general recursive functions or A-terms obeying an input output behavior specified by a number of examples. Generating complex algorithms from just a small number of simple input-output pairs is the goal of inductiveprogramming. This paper illustrates that our approach works well in some challenging examples.
inductiveprogramming systems characteristically exhibit an exponential explosion in search time as one increases the size of the programs to be generated. As a way of overcoming this, we introduce incremental learnin...
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ISBN:
(纸本)9783642119309
inductiveprogramming systems characteristically exhibit an exponential explosion in search time as one increases the size of the programs to be generated. As a way of overcoming this, we introduce incremental learning;a process in which an inductiveprogramming system automatically modifies its inductive bias towards some domain through solving a sequence of gradually more difficult problems in that domain. We demonstrate a simple form of incremental learning in which a system incorporates solution programs into its background knowledge as it progresses through a sequence of problems. Using a search-based inductive functional programming system modelled on the MagicHaskeller system of Katayama [4], we perform a set of experiments comparing the performance of inductiveprogramming with and without incremental learning. Incremental learning is shown to produce a performance improvement of at least a factor of thirty on each of the four problem sequences tested. We describe how, given some assumptions, inductiveprogramming with incremental learning can be shown to have a polynomial, rather than exponential, time complexity with respect to the size of the program to be generated. We discuss the difficulties involved in constructing suitable problem sequences for our incremental learning system, and consider what improvements can be made to overcome these difficulties.
The proceedings contain 9 papers. The topics discussed include: deriving a relationship from a single example;synthesis of functions using generic programming;inductiveprogramming: a survey of program synthesis techn...
ISBN:
(纸本)3642119301
The proceedings contain 9 papers. The topics discussed include: deriving a relationship from a single example;synthesis of functions using generic programming;inductiveprogramming: a survey of program synthesis techniques;incremental learning in inductiveprogramming;enumerating well-typed terms generically;generalisation operators for lists embedded in a metric space;porting IGORII from MAUDE to HASKELL;automated method induction: functional goes object oriented;and recent improvements of MagicHaskeller.
In some application areas, similarities and distances are used to calculate how similar two objects are in order to use these measurements to find related objects, to cluster a set of objects, to make classifications ...
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ISBN:
(纸本)9783642119309
In some application areas, similarities and distances are used to calculate how similar two objects are in order to use these measurements to find related objects, to cluster a set of objects, to make classifications or to perform an approximate search guided by the distance. In many other application areas, we require patterns to describe similarities in the data. These patterns are usually constructed through generalisation (or specialisation) operators. For every data structure, we can define distances. In fact, we may find different distances for sets, lists, atoms, numbers, ontologies, web pages, etc. We can also define pattern languages and use generalisation operators over them. However, for many data structures, distances and generalisation operators are not consistent. For instance, for lists (or sequences), edit distances are not consistent with regular languages, since, for a regular pattern such as *a, the covered set of lists might be far away in terms of the edit distance (e.g. bbbbbba and aa). In this paper we investigate the way in which, given a pattern language, we can define a pair of generalisation operator and distance which are consistent. We define the notion of (minimal) distance-based generalisation operators for lists. We illustrate positive results with two different pattern languages.
In this paper we consider an extension of Logic programming that tackles the SemanticWeb challenge of acquiring rules combined with ontologies. To face this bottleneck problem we propose a framework that resorts to th...
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Recent advances in network representation learning have sparked renewed interest in developing strategies for learning on spatiotemporal signals, crucial for applications like traffic forecasting, recommendation syste...
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ISBN:
(纸本)9798400712548
Recent advances in network representation learning have sparked renewed interest in developing strategies for learning on spatiotemporal signals, crucial for applications like traffic forecasting, recommendation systems, and social network analysis. Despite the popularity of Graph Neural Networks for node-level clustering, most specialized solutions are evaluated in transductive learning settings, where the entire graph is available during training, leaving a significant gap in understanding their performance in inductive learning settings. This work presents an experimental evaluation of community detection approaches on temporal graphs, comparing traditional methods with deep learning models geared toward node-level clustering. We assess their performance on six real-world datasets, focused on a transductive setting and extending to an inductive setting for one dataset. Our results show that deep learning models for graphs do not consistently outperform more established methods on this task, highlighting the need for more effective approaches and comprehensive benchmarks for their evaluation.
This paper describes the problems with debugging tools for answer set programming, a declarative programming paradigm. Current approaches are difficult to use on most applications due to the considerable bottlenecks i...
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This paper describes the problems with debugging tools for answer set programming, a declarative programming paradigm. Current approaches are difficult to use on most applications due to the considerable bottlenecks in communicating the reasons to the user. In this paper we examine the reasons for this and suggest some possible future directions.
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