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.
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:
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.
5629980
Winner Determination in Sequential Majority Voting
Preferences can be aggregated using voting rules. We consider here the family of rules which perform a sequence of pairwise majority comparisons between two candidates. The winner thus depends on the chosen sequence of comparisons, which can be represented by a binary tree. We address the difficulty of computing candidates that win for some trees, and then introduce and study the notion of fair winner, i.e. candidates who win in a balanced tree. We then consider the situation where we lack complete informations about preferences, and determine the computational complexity of computing winners in this case.
6689169
Calculating Criticalities
15244663
Inverse Consistencies for Non-Binary Constraints
We present a detailed study of two inverse consistencies for non-binary constraints: relational path inverse consistency (rel PIC) and pairwise inverse consistency (PWIC). These are stronger than generalized arc consistency (GAC), even though they also only prune domain values. We propose algorithms to achieve rel PIC and PWIC, that have a time complexity better than the previous generic algorithm for inverse consistencies. One of our algorithms for PWIC has a complexity comparable to that for GAC despite doing more pruning. Our experiments demonstrate that inverse consistencies can be more efficient than GAC on a range of non-binary problems.
16529131
The Difference All-Difference Makes
We perform a comprehensive theoretical and experimental analysis of the use of all-different constraints. We prove that generalized arc-consistency on such constraints lies between neighborhood inverse consistency and, under a simple restriction, path inverse consistency on the binary representation of the problem. By generalizing the arguments of Kondrak and van Beek, we prove that a search algorithm that maintains generalized arc-consistency on all-different constraints dominates a search algorithm that maintains arc-consistency on the binary representation. Our experiments show the practical value of achieving these high levels of consistency. For example, we can solve almost all benchmark quasigroup completion problems up to order 25 with just a few branches of search. These results demonstrate the benefits of using non-binary constraints like all-different to identify structure in problems.
7417163
Easy Problems are Sometimes Hard
We present a detailed experimental investigation of the easy-hard-easy phase transition for randomly generated instances of satisfiability problems. Problems in the hard part of the phase transition have been extensively used for benchmarking satisfiability algorithms. This study demonstrates that problem classes and regions of the phase transition previously thought to be easy can sometimes be orders of magnitude more difficult than the worst problems in problem classes and regions of the phase transition considered hard. These difficult problems are either hard unsatisfiable problems or are satisfiable problems which give a hard unsatisfiable subproblem following a wrong split. Whilst these hard unsatisfiable problems may have short proofs, these appear to be difficult to find, and other proofs are long and hard.