3,388 research outputs found
Learning definite Horn formulas from closure queries
A definite Horn theory is a set of n-dimensional Boolean vectors whose characteristic function is expressible as a definite Horn formula, that is, as conjunction of definite Horn clauses. The class of definite Horn theories is known to be learnable under different query learning settings, such as learning from membership and equivalence queries or learning from entailment. We propose yet a different type of query: the closure query. Closure queries are a natural extension of membership queries and also a variant, appropriate in the context of definite Horn formulas, of the so-called correction queries. We present an algorithm that learns conjunctions of definite Horn clauses in polynomial time, using closure and equivalence queries, and show how it relates to the canonical Guigues–Duquenne basis for implicational systems. We also show how the different query models mentioned relate to each other by either showing full-fledged reductions by means of query simulation (where possible), or by showing their connections in the context of particular algorithms that use them for learning definite Horn formulas.Peer ReviewedPostprint (author's final draft
The use of data-mining for the automatic formation of tactics
This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques
Semitopology: a new topological model of heterogeneous consensus
A distributed system is permissionless when participants can join and leave
the network without permission from a central authority. Many modern
distributed systems are naturally permissionless, in the sense that a central
permissioning authority would defeat their design purpose: this includes
blockchains, filesharing protocols, some voting systems, and more. By their
permissionless nature, such systems are heterogeneous: participants may only
have a partial view of the system, and they may also have different goals and
beliefs. Thus, the traditional notion of consensus -- i.e. system-wide
agreement -- may not be adequate, and we may need to generalise it.
This is a challenge: how should we understand what heterogeneous consensus
is; what mathematical framework might this require; and how can we use this to
build understanding and mathematical models of robust, effective, and secure
permissionless systems in practice?
We analyse heterogeneous consensus using semitopology as a framework. This is
like topology, but without the restriction that intersections of opens be open.
Semitopologies have a rich theory which is related to topology, but with its
own distinct character and mathematics. We introduce novel well-behavedness
conditions, including an anti-Hausdorff property and a new notion of `topen
set', and we show how these structures relate to consensus. We give a
restriction of semitopologies to witness semitopologies, which are an
algorithmically tractable subclass corresponding to Horn clause theories,
having particularly good mathematical properties. We introduce and study
several other basic notions that are specific and novel to semitopologies, and
study how known quantities in topology, such as dense subsets and closures,
display interesting and useful new behaviour in this new semitopological
context
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