11036602
Encodings of Non-Binary Constraint Satisfaction Problems
We perform a detailed theoretical and empirical comparison of the dual and hidden variable encodings of non-binary constraint satisfaction problems. We identify a simple relationship between the two encodings by showing how we can translate between the two by composing or decomposing relations. This translation suggests that we will tend to achieve more pruning in the dual than in the hidden variable encoding. We prove that achieving arc-consistency one dual encoding is strictly stronger than achieving arc-consistency on the hidden variable, and this itself is equivalent to achieving generalized arc-consistency on the original (non-binary) problem. We also prove that, as a consequence of the unusual topology of the constraint graph. In the hidden variable encoding, inverse consistencies like neighborhood. Inverse consistency and path inverse consistency collapse down onto arc-consistency. Finally, we propose the "double encoding", which combines together both the dual and the hidden variable encodings.
11070611
The Constrainedness of Arc Consistency
We show that the same methodology used to study phase transition behaviour in NP-complete problems works with a polynomial problem class: establishing arc consistency. A general measure of the constrainedness of an ensemble of problems, used to locate phase transitions in random NP-complete problems, predicts the location of a phase transition in establishing arc consistency. A complexity peak for the AC3 algorithm is associated with this transition. Finite size scaling models both the scaling of this transition and the computational cost. On problems at the phase transition, this model of computational cost agrees with the theoretical worst case. As with NP-complete problems, constrainedness — and proxies for it which are cheaper to compute — can be used as a heuristic for reducing the number of checks needed to establish arc consistency in AC3. 
 
The authors are supported by EPSRC awards GR/L/24014 and GR/K/65706, and the EU award EU20603. The authors wish to thank other members of the APES research group for their help, and Gene Freuder.
5055655
The Complexity of Global Constraints
We study the computational complexity of reasoning with global constraints. We show that reasoning with such constraints is intractable in general. We then demonstrate how the same tools of computational complexity can be used in the design and analysis of specific global constraints. In particular, we illustrate how computational complexity can be used to determine when a lesser level of local consistency should be enforced, when decomposing constraints will lose pruning, and when combining constraints is tractable. We also show how the same tools can be used to study symmetry breaking, meta-constraints like the cardinality constraint, and learning nogoods.
17485829
Beyond Finite Domains: The All Different and Global Cardinality Constraints
We describe how the propagator for the ALL-DIFFERENT constraint can be generalized to prune variables whose domains are not just simple finite integer domains. We show, for example, how it can be used to propagate set, multiset and tuple variables.
2156641
Multiset Ordering Constraints
We identify a new and important global (or nonbinary) constraint which ensures that the values taken by two vectors of variables, when viewed as multisets, are ordered. This constraint is useful for a number of different applications including breaking symmetry and fuzzy constraint satisfaction. We propose and implement a linear time algorithm for enforcing generalised arc-consistency on such a multiset ordering constraint. Experimental results show considerable promise.
12061015
Automatic Invention of Integer Sequences
We report on the application of the HR program (Colton, Bundy, & Walsh 1999) to the problem of automatically inventing integer sequences. Seventeen sequences invented by HR are interesting enough to have been accepted into the Encyclopedia of Integer Sequences (Sloane 2000) and all were supplied with interesting conjectures about their nature, also discovered by HR. By extending HR, we have enabled it to perform a two stage process of invention and investigation. This involves generating both the definition and terms of a new sequence, relating it to sequences already in the Encyclopedia and pruning the output to help identify the most surprising and interesting results.
17164164
Towards Measuring Similarity in Description Logics
We review several kinds of previously studied concept similarity measures, and then rephrase them in terms of a simple DL. We discuss the difficulties encountered in trying to generalize these formulations to more complex DLs, and settle on one based on probability/information theory as being the most principled. 1 Motivation and Goals The idea of measuring concept similarity has received considerable attention in several domains, including psychology, cognitive science, and computational linguistics and information retrieval. This work has been applied and extended recently in the field of Information Integration, which often relies on ontologies and hence concepts described in DLs. Most past work has concentrated on the similarity of “atomic” concepts (word senses), rather than composite, defined concepts, which are the stock-in-trade of DLs. This has sometimes been characterized as the difference between considering nouns appearing in a dictionary, e.g. WordNet, and general arbitrary noun phrases. The modest goal of this paper is to consider the problem of generalizing previous efforts to define similarity for primitive concepts to composite ones. To this end, we review three classes of approaches found in the current literature, rephrase them in terms of a very simple description logic, and then try to generalize this to a complex DL. These extensions invite a host of new questions that we leave open for further research. To begin with, we make several observations about possible real-valued function(s) sim(C,D) that would measure concept similarity in general. First, any effort to assess simlarity via absolute numeric values seems ill-advised — in small experiments even human judgments have been shown to correlate only in the .91 to .94 range. A less exacting target is to use sim to provide relative orderings over concepts. Some researchers have studied a complementary measure, distance dist(C,D), which can similarly be used for ordering alternatives. To the extent that two functions provide similar orderings (e.g., consistently differing by a constant factor) they are essentially indistinguishable for our purposes. Nonetheless, there are two important general properties that are widely agreed to be desirable for sim(C,D): 1. sim(C,D) is positively correlated with the amount of commonality between C and D. 2. sim(C,D) is negatively correlated with the amount of difference between C and D. The key question then becomes how to measure “commonality”; in turn, this is related to how information about the concept is captured. 2 Three approaches to concept similarity Clearly, in order to compare concepts we need to consider what information we have available about them. We follow Rodriguez [14] in categorizing a variety of approaches, and for each we give a few of the best known functions, which we shall follow. Feature-based Models In this approach, a concept C is characterized by a a set of features ftrs(C). In his pioneering work on similarity, the psychologist Amos Tversky [16] introduced two families of measures: the “contrast model”, where the similarity of C and D is a linear function: contrasttv(C,D) = θf(ftrs(C) ∩ ftrs(D)) − αf(ftrs(C) \ ftrs(D))− βf(ftrs(D) \ ftrs(C)), where \ is set difference, θ, α, and β are non-negative constants, and where f(.) is often taken as the count of features in the set (written |.|). Tversky also proposed a normalized “ratio model”, where similarity is a fraction involving these sets:
3106079
Efficient Exploration With Latent Structure
When interacting with a new environment, a robot can improve its online performance by efficiently exploring the effects of its actions. The efficiency of exploration can be expanded significantly by modeling and using latent structure to generalize experiences. We provide a theoretical development of the problem of exploration with latent structure, analyze several algorithms and prove matching lower bounds. We demonstrate our algorithmic ideas on a simple robot car repeatedly traversing a path with two different surface properties.
14691140
Counting unlabelled three-connected and homeomorphically irreducible two-connected graphs
A graph will be assumed to be finite and unoriented, with no loops or multiple edges; if multiple edges are to be allowed, the term multigraph will be used. A graph or multigraph. will be called k-connected if at least k vertices and their incident edges must be removed to disconnect it (a complete graph is considered to be k-connected for any k). A block (respectively, multiblock) is a 2-connected graph (respectively, multigraph) with at least 2 vertices, and a brick is a 3-connected graph with at least 4 vertices. A Iabelling of a graph or multigraph with n vertices is a l-l correspondence from the set { 1, 2,..., n} onto the set of its vertices. Let A(x, y) be the mixed exponential generating function C,,, An,,,,x”.vmln!, where A,,, is the number of labelled graphs with n vertices and m edges, and let C(x, y) and B(x, y) be analogous generating functions which count labelled connected graphs and labelled blocks, respectively. The following formulae, due to Riddell [lo], appear in one-variable form in [6, pp. 3-111:
1960492
Challenges in securing voice over IP
Although VoIP offers lower cost and greater flexibility, it can also introduce significant risks and vulnerabilities. This article explains the challenges of VoIP security and outlines steps for helping to secure an organization's VoIP network.
15854568
Enumeration of eulerian and unicursal planar maps
Sum-free enumerative formulae are derived for several classes of rooted planar maps with no vertices of odd valency (eulerian maps) and with two vertices of odd valency (unicursal maps). As corollaries we obtain simple formulae for the numbers of unrooted eulerian and unicursal planar maps. Also, we obtain a sum-free formula for the number of rooted bi-eulerian (eulerian and bipartite) maps and some related results.