In this chapter we will present the basic concepts of term rewriting that are needed in this book. More details on term rewriting, its applications, and related subjects can be found in the textbook of Baader and Nipkow [BN98]. Readers versed in German are also referred to the textbooks of Avenhaus [Ave95], Bundgen [Bun98], and Drosten [Dro89]. Moreover, there are several survey articles [HO80, DJ90, Klo92, Pla93] that can also be consulted.

Term Rewriting Systems

Dependent type theories have a long history of being used for theorem proving One aspect of type theory which makes it very powerful as a proof language is that it mixes deduction with computation This also makes type theory a good candidate for programming---the strength of the type system allows properties of programs to be stated and established, and the computational properties provide semantics for the programs

This thesis is concerned with bridging the gap between the theoretical presentations of type theory and the requirements on a practical programming language Although there are many challenging research problems left to solve before we have an industrial scale programming language based on type theory, this thesis takes us a good step along the way

/pdf/towards-a-practical-programming-language-based-on-dependent-3lmfc1es25.pdf

Towards a practical programming language based on dependent type theory

What do you do to start reading automatic sequences theory applications generalizations? Searching the book that you love to read first or find an interesting book that will make you want to read? Everybody has difference with their reason of reading a book. Actuary, reading habit must be from earlier. Many people may be love to read, but not a book. It's not fault. Someone will be bored to open the thick book with small words to read. In more, this is the real condition. So do happen probably with this automatic sequences theory applications generalizations.

Automatic sequences. Theory, applications, generalizations.

Some readers know to play the game of nim well, fewer play a perfect annihilation game, and nobody knows whether there exists an opening move in chess that will guarantee a win for white. These games and many more, belong to the family of combinatorial games, by which we mean the set of all two-player perfect-information games without chance moves and with outcomes lose or win (and sometimes: dynamic tie). The motivation for ONAG may have been, and perhaps was-and I would like to think that it was-the attempt to bridge the theory gap between nim-like and chess-like games. Why is there a gap? Every combinatorial game can be described as a directed graph called game-graph, whose vertices are the game positions, and (u, v) is a directed edge if and only if there is a move from position u to position v. Denote by N the set of all positions from which the Next (first) player can force a win; by P the set of all positions from which the Previous (second) player can force a win; and by T the set of all (dynamic) Tie positions, which are positions from which no player can force a win and therefore both can avoid losing. In an acyclic game-graph there cannot be any tie positions. The N, P, T classification of any game graph R = (V, E) can be determined in 0(\V\ + \E\) steps [8]. For both nim and chess, a finite game-graph can be constructed and the N, P, T classification can be determined. So both games are solvable in principle. If we play nim with n piles, each pile containing at most k tokens, then the game-graph contains (k + \) vertices. Suppose that in (generalized) chess played on an « X « board there are k different pieces. If k is about n/2, then the game-graph of chess contains O (2") vertices. So both game-graphs have exponentially many vertices, and thus both games appear intractable in the usual sense of computational complexity [1, Chapter 10], [14, Chapter 9], namely a computation appears to be required which is asymptotically exponential. From a computational efficiency standpoint, the essential difference between nim and chess is that nim can be viewed as a disjunctive compound (sum) of independent games, namely the individual piles. A disjunctive

http://ieeexplore.ieee.org/iel5/5/31266/01455274.pdf

On numbers and games

Sledgehammer is a highly successful subsystem of Isabelle/HOL that calls automatic theorem provers to assist with interactive proof construction. It requires no user configuration: it can be invoked with a single mouse gesture at any point in a proof. It automatically finds relevant lemmas from all those currently available. An unusual aspect of its architecture is its use of unsound translations, coupled with its delivery of results as Isabelle/HOL proof scripts: its output cannot be trusted, but it does not need to be trusted. Sledgehammer works well with Isar structured proofs and allows beginners to prove challenging theorems.

https://www.cl.cam.ac.uk/~lp15/papers/Automation/paar.pdf

Three Years of Experience with Sledgehammer, a Practical Link between Automatic and Interactive Theorem Provers

We give an algorithm for deciding productivity of a large and natural class of recursive stream definitions.
A stream definition is called ‘productive’ if it can be evaluated continually in such a way that a uniquely
determined stream in constructor normal form is obtained as the limit. Whereas productivity is undecidable
for stream definitions in general, we show that it can be decided for ‘pure’ stream definitions. For every pure
stream definition the process of its evaluation can be modelled by the dataflow of abstract stream elements,
called ‘pebbles’, in a finite ‘pebbleflow net(work)’. And the production of a pebbleflow net associated with
a pure stream definition, that is, the amount of pebbles the net is able to produce at its output port, can
be calculated by reducing nets to trivial nets.

Productivity of stream definitions

Automated Proof Construction in Type Theory Using Resolution

We present a coinductive framework for defining infinitary analogues of equational reasoning and rewriting in a uniform way. We define the relation 1, a notion of infinitary equational reasoning,

A Coinductive Framework for Infinitary Rewriting and Equational Reasoning (Extended Version )

We give an algorithm for deciding productivity of a large and natural class of recursive stream definitions. A stream definition is called 'productive' if it can be evaluated continuously in such a way that a uniquely determined stream is obtained as the limit. Whereas productivity is undecidable for stream definitions in general, we show that it can be decided for 'pure' stream definitions. For every pure stream definition the process of its evaluation can be modelled by the dataflow of abstract stream elements, called 'pebbles', in a finite 'pebbleflow net(work)'. And the production of a pebbleflow net associated with a pure stream definition, that is, the amount of pebbles the net is able to produce at its output port, can be calculated by reducing nets to trivial nets.

/pdf/productivity-of-stream-definitions-4cod0phjh3.pdf

In functional programming languages the use of infinite structures is common practice. For total correctness of programs dealing with infinite structures one must guarantee that every finite part of the result can be evaluated in finitely many steps. This is known as productivity. For programming with infinite structures, productivity is what termination in well-defined results is for programming with finite structures. 
Fractran is a simple Turing-complete programming language invented by Conway. We prove that the question whether a Fractran program halts on all positive integers is Pi^0_2-complete. In functional programming, productivity typically is a property of individual terms with respect to the inbuilt evaluation strategy. By encoding Fractran programs as specifications of infinite lists, we establish that this notion of productivity is Pi^0_2-complete even for the most simple specifications. Therefore it is harder than termination of individual terms. In addition, we explore possible generalisations of the notion of productivity in the framework of term rewriting, and prove that their computational complexity is Pi^1_1-complete, thus exceeding the expressive power of first-order logic.

Complexity of Fractran and Productivity

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Papers

Productivity of stream definitions

Automated Proof Construction in Type Theory Using Resolution

A Coinductive Framework for Infinitary Rewriting and Equational Reasoning (Extended Version )

Productivity of stream definitions

Complexity of Fractran and Productivity