Rewriting logic can be used to specify a wide range of real-time and hybrid systems under a variety of time models, including discrete and dense time models. The Real-Time Maude tool, built on top of the Maude rewriti...
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We approach the general problem of representing higher-order languages, that are usually equipped with special variable binding constructs, in a less specialized first-order framework such as membership equational log...
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Parameterized specification and programming is a key modularity and reusability technique crucial for managing the complexity of large specifications and programs. In the search for ever more powerful parameterized mo...
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Service robots require interactive programming interfaces that allow users without programming experience to easily instruct the robots. Systems following the programming by Demonstration (PbD) paradigm that were deve...
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ISBN:
(纸本)3540669337
Service robots require interactive programming interfaces that allow users without programming experience to easily instruct the robots. Systems following the programming by Demonstration (PbD) paradigm that were developed within the last years are getting closer to this goal. However, most of these systems lack the possibility for the user to supervise and influence the process of program generation after the initial demonstration was performed. In this paper an approach is presented, that enables the user to supervise the entire program generation process and to annotate, and edit system hypotheses. Moreover, the knowledgerepresentation and algorithms presented enable the user to generalise the generated program by annotating conditions and object selection criteria via a 3D simulation and graphical user interface. The resulting PbD-system widens the PbD approach in robotics to programming based on human demonstrations and user annotations.
We present a method for discovering new knowledge from structural data which are represented by graphs in the framework of inductive logicprogramming. A graph, or network, is widely used for representing relations be...
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ISBN:
(纸本)3540661093
We present a method for discovering new knowledge from structural data which are represented by graphs in the framework of inductive logicprogramming. A graph, or network, is widely used for representing relations between various data and expressing a small and easily understandable hypothesis. Formal Graph System (FGS) is a kind of logicprogramming system which directly deals with graphs just like first order terms. By employing refutably inductive inference algorithms and graph algorithmic techniques, we are developing a knowledge discovery system KD-FGS, which acquires knowledge directly from graph data by using FGS as a knowledgerepresentation language. In this paper we develop a logical foundation of our knowledge discovery system. A term tree is a pattern which consists of variables and tree-like structures. We give a polynomial-time algorithm for finding a unifier of a term tree and a tree in order to make consistency checks efficiently. Moreover we give experimental results on some graph theoretical notions with the system. The experiments show that the system is useful for finding new knowledge.
Virtual assembly allows human operators to use their hands manipulating the graphics representation of machinery parts and programming assembly tasks directly in a three-dimensional operation space. The imprecise meas...
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In this paper, we use autoepistemic reasoning semantics to classify various semantics for disjunctive logic programs with default negation. We have observed that two different types of negative introspection in autoep...
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ISBN:
(纸本)3540649581
In this paper, we use autoepistemic reasoning semantics to classify various semantics for disjunctive logic programs with default negation. We have observed that two different types of negative introspection in autoepistemic reasoning present two different interpretations of default negation: consistency-based and minimal-model-based. We also observed that all logic program semantics fall into three semantical points of view: the skeptical, stable, and partial-stable. Based on these two observations, we classify disjunctive logic program semantics into six different categories, and discuss the relationships among various semantics.
The proceedings contain 9 papers. The topics discussed include: knowledgerepresentation with logic programs;DATALOG with nested rules;partial evidential stable models for disjunctive deductive databases;disjunctive l...
ISBN:
(纸本)3540649581
The proceedings contain 9 papers. The topics discussed include: knowledgerepresentation with logic programs;DATALOG with nested rules;partial evidential stable models for disjunctive deductive databases;disjunctive logicprogramming and autoepistemic logic;a system for abductive learning of logic programs;refining action theories through abductive logicprogramming;abduction, argumentation and bi-disjunctive logic programs;reasoning with prioritized defaults;and generalizing updates: from models to programs.
This paper presents an extension of disjunctive dat slog (Datalog(V)) by nested rules. Nested rules are (disjunctive) rules where elements of the head may be also rules. Nested rules increase the knowledge representat...
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ISBN:
(纸本)3540649581
This paper presents an extension of disjunctive dat slog (Datalog(V)) by nested rules. Nested rules are (disjunctive) rules where elements of the head may be also rules. Nested rules increase the knowledgerepresentation power of Datalog(V) both from a theoretical and from a practical viewpoint. A number of examples show that nested rules allow to naturally model several real world situations that cannot be represented in Datalog(V). An in depth analysis of complexity and expressive power of the language shows that nested rules do increase the expressiveness of Datalog(V) without implying any increase in its computational complexity.
We present the system LAP (Learning Abductive Programs) that is able to learn abductive logic programs from examples and from a background abductive theory. A new type of induction problem has been defined as an exten...
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ISBN:
(纸本)3540649581
We present the system LAP (Learning Abductive Programs) that is able to learn abductive logic programs from examples and from a background abductive theory. A new type of induction problem has been defined as an extension of the Inductive logicprogramming framework. In the new problem definition, both the background and the target theories are abductive logic programs and abductive derivability is used as the coverage relation. LAP is based on the basic top-down ILP algorithm that has been suitably extended. In particular, coverage of examples is tested by using the abductive proof procedure defined by Kakas and Mancarella [24]. Assumptions can be made in order to cover positive examples and to avoid the coverage of negative ones. and these assumptions can be used as new training data. LAP can be applied for learning in the presence of incomplete knowledge and for learning exceptions to classification rules.
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