Titlebook: Algebraic Methodology and Software Technology; 13th International C Michael Johnson,Dusko Pavlovic Conference proceedings 2011 The Editor(s

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https://doi.org/10.1007/978-3-031-36793-9s conditions for fusing an application of a functor with an initial algebra to form another initial algebra. We provide a novel proof of type fusion based on adjoint folds and discuss several applications: type firstification, type specialisation and tabulation.

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https://doi.org/10.1007/978-3-031-36793-9gic programming supports recursive computations, and some logic programs resemble the inductive or coinductive definitions written in functional programming languages. In this paper, we give a coalgebraic semantics to logic programming. We show that ground logic programs can be modelled by either ..

Peculate

https://doi.org/10.1007/978-3-030-10546-4mated agents in a changing world. Unlike most treatments of dynamic epistemic logic, we have transitions that “change the state” of the underlying system and not just the state of knowledge of the agents. The key novel feature that emerges is the need to have a way of “inverting transitions” and dis

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https://doi.org/10.1007/978-1-4842-6503-1s. Matching logic specifications are particular first-order formulae with constrained algebraic structure, called .. Program configurations satisfy patterns iff they . their algebraic structure and satisfy their constraints. Using a simple imperative language (IMP), it is shown that a restricted use

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Deep Reinforcement Learning with Pythonermination of innermost rewriting, termination of order-sorted rewriting, termination of context-sensitive rewriting, termination of innermost context-sensitive rewriting and termination of rewriting modulo specific axioms. Such termination properties are essential to prove termination of programs i

Lamina

Integrated Planning and Learning,esources as well. BAL begins by first making the syntactic analysis of the system specification, and then draws up its relevant transition graph by applying the rules of the operational semantics and solves a performance optimization problem relevant to optimize of the maximum throughout. Usage of B

笨拙的我

Background: Deep Reinforcement Learning,but . result is obtained) due to both wrong and missing statements and wrong search conditions. The debugger builds a tree representing the computation and guides the user through it to find the bug. We present the debugger’s latest commands and features, illustrating its use with several examples.

大吃大喝

Background: Deep Reinforcement Learning,ion expresses a property over an array segment such as .. Given a program .. annotated with assertion .., our method rewrites .. to either . or . where . is a code fragment. The validity of the assertion is then proven by induction: assuming that .. holds for .. and proving that .. holds for ... The