OpenRaiser/Intern-Atlas huggingface dataset:
Integrating Case-Based and Rule-Based Reasoning: the Possibilistic Connection
Venue: arXiv cs.AI 2013
Year: 2013

Rule based reasoning (RBR) and case based reasoning (CBR) have emerged as two important and complementary reasoning methodologies in artificial intelligence (Al). For problem solving in complex, real world situations, it is useful to integrate RBR and CBR. This paper presents an approach to achieve a compact and seamless integration of RBR and CBR within the base architecture of rules. The paper focuses on the possibilistic nature of the approximate reasoning methodology common to both CBR and RBR. In CBR, the concept of similarity is casted as the complement of the distance between cases. In RBR the transitivity of similarity is the basis for the approximate deductions based on the generalized modus ponens. It is shown that the integration of CBR and RBR is possible without altering the inference engine of RBR. This integration is illustrated in the financial domain of mergers and acquisitions. These ideas have been implemented in a prototype system called MARS.
The Transferable Belief Model and Other Interpretations of Dempster-Shafer's Model
Venue: arXiv cs.AI 2013
Year: 2013

Dempster-Shafer's model aims at quantifying degrees of belief But there are so many interpretations of Dempster-Shafer's theory in the literature that it seems useful to present the various contenders in order to clarify their respective positions. We shall successively consider the classical probability model, the upper and lower probabilities model, Dempster's model, the transferable belief model, the evidentiary value model, the provability or necessity model. None of these models has received the qualification of Dempster-Shafer. In fact the transferable belief model is our interpretation not of Dempster's work but of Shafer's work as presented in his book (Shafer 1976, Smets 1988). It is a ?purified' form of Dempster-Shafer's model in which any connection with probability concept has been deleted. Any model for belief has at least two components: one static that describes our state of belief, the other dynamic that explains how to update our belief given new pieces of information. We insist on the fact that both components must be considered in order to study these models. Too many authors restrict themselves to the static component and conclude that Dempster-Shafer theory is the same as some other theory. But once the dynamic component is considered, these conclusions break down. Any comparison based only on the static component is too restricted. The dynamic component must also be considered as the originality of the models based on belief functions lies in its dynamic component.
Towards a Normative Theory of Scientific Evidence
Venue: arXiv cs.AI 2013
Year: 2013

A scientific reasoning system makes decisions using objective evidence in the form of independent experimental trials, propositional axioms, and constraints on the probabilities of events. As a first step towards this goal, we propose a system that derives probability intervals from objective evidence in those forms. Our reasoning system can manage uncertainty about data and rules in a rule based expert system. We expect that our system will be particularly applicable to diagnosis and analysis in domains with a wealth of experimental evidence such as medicine. We discuss limitations of this solution and propose future directions for this research. This work can be considered a generalization of Nilsson's "probabilistic logic" [Nil86] to intervals and experimental observations.
A Model for Non-Monotonic Reasoning Using Dempster's Rule
Venue: arXiv cs.AI 2013
Year: 2013

Considerable attention has been given to the problem of non-monotonic reasoning in a belief function framework. Earlier work (M. Ginsberg) proposed solutions introducing meta-rules which recognized conditional independencies in a probabilistic sense. More recently an e-calculus formulation of default reasoning (J. Pearl) shows that the application of Dempster's rule to a non-monotonic situation produces erroneous results. This paper presents a new belief function interpretation of the problem which combines the rules in a way which is more compatible with probabilistic results and respects conditions of independence necessary for the application of Dempster's combination rule. A new general framework for combining conflicting evidence is also proposed in which the normalization factor becomes modified. This produces more intuitively acceptable results.
Interaction Nets in Russian
Venue: arXiv cs.LO 2013
Year: 2013

Draft translation to Russian of Chapter 7, Interaction-Based Models of Computation, from Models of Computation: An Introduction to Computability Theory by Maribel Fernandez. "In this chapter, we study interaction nets, a model of computation that can be seen as a representative of a class of models based on the notion of 'computation as interaction'. Interaction nets are a graphical model of computation devised by Yves Lafont in 1990 as a generalisation of the proof structures of linear logic. It can be seen as an abstract formalism, used to define algorithms and analyse their cost, or as a low-level language into which other programming languages can be compiled. This is fruitful because interaction nets can be implemented with reasonable efficiency."
Map Learning with Indistinguishable Locations
Venue: arXiv cs.AI 2013
Year: 2013

Nearly all spatial reasoning problems involve uncertainty of one sort or another. Uncertainty arises due to the inaccuracies of sensors used in measuring distances and angles. We refer to this as directional uncertainty. Uncertainty also arises in combining spatial information when one location is mistakenly identified with another. We refer to this as recognition uncertainty. Most problems in constructing spatial representations (maps) for the purpose of navigation involve both directional and recognition uncertainty. In this paper, we show that a particular class of spatial reasoning problems involving the construction of representations of large-scale space can be solved efficiently even in the presence of directional and recognition uncertainty. We pay particular attention to the problems that arise due to recognition uncertainty.
Plan Recognition in Stories and in Life
Venue: arXiv cs.AI 2013
Year: 2013

Plan recognition does not work the same way in stories and in "real life" (people tend to jump to conclusions more in stories). We present a theory of this, for the particular case of how objects in stories (or in life) influence plan recognition decisions. We provide a Bayesian network formalization of a simple first-order theory of plans, and show how a particular network parameter seems to govern the difference between "life-like" and "story-like" response. We then show why this parameter would be influenced (in the desired way) by a model of speaker (or author) topic selection which assumes that facts in stories are typically "relevant".
Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources
Venue: arXiv cs.AI 2013
Year: 2013

We introduce a graceful approach to probabilistic inference called bounded conditioning. Bounded conditioning monotonically refines the bounds on posterior probabilities in a belief network with computation, and converges on final probabilities of interest with the allocation of a complete resource fraction. The approach allows a reasoner to exchange arbitrary quantities of computational resource for incremental gains in inference quality. As such, bounded conditioning holds promise as a useful inference technique for reasoning under the general conditions of uncertain and varying reasoning resources. The algorithm solves a probabilistic bounding problem in complex belief networks by breaking the problem into a set of mutually exclusive, tractable subproblems and ordering their solution by the expected effect that each subproblem will have on the final answer. We introduce the algorithm, discuss its characterization, and present its performance on several belief networks, including a complex model for reasoning about problems in intensive-care medicine.
Inference Policies
Venue: arXiv cs.AI 2013
Year: 2013

It is suggested that an AI inference system should reflect an inference policy that is tailored to the domain of problems to which it is applied -- and furthermore that an inference policy need not conform to any general theory of rational inference or induction. We note, for instance, that Bayesian reasoning about the probabilistic characteristics of an inference domain may result in the specification of an nonBayesian procedure for reasoning within the inference domain. In this paper, the idea of an inference policy is explored in some detail. To support this exploration, the characteristics of some standard and nonstandard inference policies are examined.
Maximum Uncertainty Procedures for Interval-Valued Probability Distributions
Venue: arXiv cs.AI 2013
Year: 2013

Measures of uncertainty and divergence are introduced for interval-valued probability distributions and are shown to have desirable mathematical properties. A maximum uncertainty inference procedure for marginal interval distributions is presented. A technique for reconstruction of interval distributions from projections is developed based on this inference procedure
Freedom: A Measure of Second-order Uncertainty for Intervalic Probability Schemes
Venue: arXiv cs.AI 2013
Year: 2013

This paper discusses a new measure that is adaptable to certain intervalic probability frameworks, possibility theory, and belief theory. As such, it has the potential for wide use in knowledge engineering, expert systems, and related problems in the human sciences. This measure (denoted here by F) has been introduced in Smithson (1988) and is more formally discussed in Smithson (1989a)o Here, I propose to outline the conceptual basis for F and compare its properties with other measures of second-order uncertainty. I will argue that F is an indicator of nonspecificity or alternatively, of freedom, as distinguished from either ambiguity or vagueness.
Normalization and the Representation of Nonmonotonic Knowledge in the Theory of Evidence
Venue: arXiv cs.AI 2013
Year: 2013

We discuss the Dempster-Shafer theory of evidence. We introduce a concept of monotonicity which is related to the diminution of the range between belief and plausibility. We show that the accumulation of knowledge in this framework exhibits a nonmonotonic property. We show how the belief structure can be used to represent typical or commonsense knowledge.
Multiscale Fractal Descriptors Applied to Texture Classification
Venue: arXiv cs.CV 2013
Year: 2013

This work proposes the combination of multiscale transform with fractal descriptors employed in the classification of gray-level texture images. We apply the space-scale transform (derivative + Gaussian filter) over the Bouligand-Minkowski fractal descriptors, followed by a threshold over the filter response, aiming at attenuating noise effects caused by the final part of this response. The method is tested in the classification of a well-known data set (Brodatz) and compared with other classical texture descriptor techniques. The results demonstrate the advantage of the proposed approach, achieving a higher success rate with a reduced amount of descriptors.
Hiding Image in Image by Five Modulus Method for Image Steganography
Venue: arXiv cs.MM 2013
Year: 2013

This paper is to create a practical steganographic implementation to hide color image (stego) inside another color image (cover). The proposed technique uses Five Modulus Method to convert the whole pixels within both the cover and the stego images into multiples of five. Since each pixels inside the stego image is divisible by five then the whole stego image could be divided by five to get new range of pixels 0..51. Basically, the reminder of each number that is not divisible by five is either 1,2,3 or 4 when divided by 5. Subsequently, then a 4-by-4 window size has been implemented to accommodate the proposed technique. For each 4-by-4 window inside the cover image, a number from 1 to 4 could be embedded secretly from the stego image. The previous discussion must be applied separately for each of the R, G, and B arrays. Moreover, a stego-key could be combined with the proposed algorithm to make it difficult for any adversary to extract the secret image from the cover image. Based on the PSNR value, the extracted stego image has high PSNR value. Hence this new steganography algorithm is very efficient to hide color images.
The freeness problem over matrix semigroups and bounded languages
Venue: arXiv cs.DM 2013
Year: 2013

We study the freeness problem for matrix semigroups. We show that the freeness problem is decidable for upper-triangular $2\times 2$ matrices with rational entries when the products are restricted to certain bounded languages.
Probability Aggregates in Probability Answer Set Programming
Venue: arXiv cs.AI 2013
Year: 2013

Probability answer set programming is a declarative programming that has been shown effective for representing and reasoning about a variety of probability reasoning tasks. However, the lack of probability aggregates, e.g. {\em expected values}, in the language of disjunctive hybrid probability logic programs (DHPP) disallows the natural and concise representation of many interesting problems. In this paper, we extend DHPP to allow arbitrary probability aggregates. We introduce two types of probability aggregates; a type that computes the expected value of a classical aggregate, e.g., the expected value of the minimum, and a type that computes the probability of a classical aggregate, e.g, the probability of sum of values. In addition, we define a probability answer set semantics for DHPP with arbitrary probability aggregates including monotone, antimonotone, and nonmonotone probability aggregates. We show that the proposed probability answer set semantics of DHPP subsumes both the original probability answer set semantics of DHPP and the classical answer set semantics of classical disjunctive logic programs with classical aggregates, and consequently subsumes the classical answer set semantics of the original disjunctive logic programs. We show that the proposed probability answer sets of DHPP with probability aggregates are minimal probability models and hence incomparable, which is an important property for nonmonotonic probability reasoning.
Excluding cycles with a fixed number of chords
Venue: arXiv cs.DM 2013
Year: 2013

Trotignon and Vuskovic completely characterized graphs that do not contain cycles with exactly one chord. In particular, they show that such a graph G has chromatic number at most max(3,w(G)). We generalize this result to the class of graphs that do not contain cycles with exactly two chords and the class of graphs that do not contain cycles with exactly three chords. More precisely we prove that graphs with no cycle with exactly two chords have chromatic number at most 6. And a graph G with no cycle with exactly three chords have chromatic number at most max(96,w(G)+1).
A near-optimal approximation algorithm for Asymmetric TSP on embedded graphs
Venue: arXiv cs.DS 2013
Year: 2013

We present a near-optimal polynomial-time approximation algorithm for the asymmetric traveling salesman problem for graphs of bounded orientable or non-orientable genus. Our algorithm achieves an approximation factor of O(f(g)) on graphs with genus g, where f(n) is the best approximation factor achievable in polynomial time on arbitrary n-vertex graphs. In particular, the O(log(n)/loglog(n))-approximation algorithm for general graphs by Asadpour et al. [SODA 2010] immediately implies an O(log(g)/loglog(g))-approximation algorithm for genus-g graphs. Our result improves the O(sqrt(g)*log(g))-approximation algorithm of Oveis Gharan and Saberi [SODA 2011], which applies only to graphs with orientable genus g; ours is the first approximation algorithm for graphs with bounded non-orientable genus. Moreover, using recent progress on approximating the genus of a graph, our O(log(g) / loglog(g))-approximation can be implemented even without an embedding when the input graph has bounded degree. In contrast, the O(sqrt(g)*log(g))-approximation algorithm of Oveis Gharan and Saberi requires a genus-g embedding as part of the input. Finally, our techniques lead to a O(1)-approximation algorithm for ATSP on graphs of genus g, with running time 2^O(g)*n^O(1).
Managing Interference Correlation Through Random Medium Access
Venue: arXiv cs.NI 2013
Year: 2013

The capacity of wireless networks is fundamentally limited by interference. However, little research has focused on the interference correlation, which may greatly increase the local delay (namely the number of time slots required for a node to successfully transmit a packet). This paper focuses on the question that whether increasing randomness in the MAC, such as frequency-hopping multiple access (FHMA) and ALOHA, helps to reduce the effect of interference correlation. We derive closed-form results for the mean and variance of the local delay for the two MAC protocols and evaluate the optimal parameters that minimize the mean local delay. Based on the optimal parameters, we propose the definitions of two operation regimes: correlation-limited regime and bandwidth-limited regime. Our results reveal that while the mean local delays for FHMA with N sub-bands and for ALOHA with transmit probability p are the same when p=1/N with thermal noise ignored, significant difference exists between the variances. At last, we evaluate the mean delay-jitter tradeoff and the bounds on the tail probability of the local delay, which shed key insights into the system design.
The implementation of the parallel shortest vector enumerate in the block Korkin-Zolotarev method
Venue: arXiv cs.DM 2013
Year: 2013

This article present a parallel CPU implementation of Kannan algorithm for solving shortest vector problem in Block Korkin-Zolotarev lattice reduction method. Implementation based on Native POSIX Thread Library and show linear decrease of runtime from number of threads.
Distributed Space-Time Coding Based on Adjustable Code Matrices for Cooperative MIMO Relaying Systems
Venue: arXiv cs.IT 2013
Year: 2013

An adaptive distributed space-time coding (DSTC) scheme is proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receive filters and adjustable code matrices are considered subject to a power constraint with an amplify-and-forward (AF) cooperation strategy. In the proposed adaptive DSTC scheme, an adjustable code matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. The effects of the limited feedback and the feedback errors are assessed. Linear MMSE expressions are devised to compute the parameters of the adjustable code matrix and the linear receive filters. Stochastic gradient (SG) and least-squares (LS) algorithms are also developed with reduced computational complexity. An upper bound on the pairwise error probability analysis is derived and indicates the advantage of employing the adjustable code matrices at the relay nodes. An alternative optimization algorithm for the adaptive DSTC scheme is also derived in order to eliminate the need for the feedback. The algorithm provides a fully distributed scheme for the adaptive DSTC at the relay node based on the minimization of the error probability. Simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.
Resolution structure in HornSAT and CNFSAT
Venue: arXiv cs.CC 2013
Year: 2013

This article describes about the difference of resolution structure and size between HornSAT and CNFSAT. We can compute HornSAT by using clauses causality. Therefore we can compute proof diagram by using Log space reduction. But we must compute CNFSAT by using clauses correlation. Therefore we cannot compute proof diagram by using Log space reduction, and reduction of CNFSAT is not P-Complete.
On Creativity of Slime Mould
Venue: arXiv cs.ET 2013
Year: 2013

Slime mould Physarum polycephalum is large single cell with intriguingly smart behaviour. The slime mould shows outstanding abilities to adapt its protoplasmic network to varying environmental conditions. The slime mould can solve tasks of computational geometry, image processing, logics and arithmetics when data are represented by configurations of attractants and repellents. We attempt to map behavioural patterns of slime onto the cognitive control versus schizotypy spectrum phase space and thus interpret slime mould's activity in terms of creativity.
A GOST-like Blind Signature Scheme Based on Elliptic Curve Discrete Logarithm Problem
Venue: arXiv cs.CR 2013
Year: 2013

In this paper, we propose a blind signature scheme and three practical educed schemes based on elliptic curve discrete logarithm problem. The proposed schemes impart the GOST signature structure and utilize the inherent advantage of elliptic curve cryptosystems in terms of smaller key size and lower computational overhead to its counterpart public key cryptosystems such as RSA and ElGamal. The proposed schemes are proved to be secure and have less time complexity in comparison with the existing schemes.
Solving Linear Equations by Classical Jacobi-SR Based Hybrid Evolutionary Algorithm with Uniform Adaptation Technique
Venue: arXiv cs.NE 2013
Year: 2013

Solving a set of simultaneous linear equations is probably the most important topic in numerical methods. For solving linear equations, iterative methods are preferred over the direct methods especially when the coefficient matrix is sparse. The rate of convergence of iteration method is increased by using Successive Relaxation (SR) technique. But SR technique is very much sensitive to relaxation factor, ω. Recently, hybridization of classical Gauss-Seidel based successive relaxation technique with evolutionary computation techniques have successfully been used to solve large set of linear equations in which relaxation factors are self-adapted. In this paper, a new hybrid algorithm is proposed in which uniform adaptive evolutionary computation techniques and classical Jacobi based SR technique are used instead of classical Gauss-Seidel based SR technique. The proposed Jacobi-SR based uniform adaptive hybrid algorithm, inherently, can be implemented in parallel processing environment efficiently. Whereas Gauss-Seidel-SR based hybrid algorithms cannot be implemented in parallel computing environment efficiently. The convergence theorem and adaptation theorem of the proposed algorithm are proved theoretically. And the performance of the proposed Jacobi-SR based uniform adaptive hybrid evolutionary algorithm is compared with Gauss-Seidel-SR based uniform adaptive hybrid evolutionary algorithm as well as with both classical Jacobi-SR method and Gauss-Seidel-SR method in the experimental domain. The proposed Jacobi-SR based hybrid algorithm outperforms the Gauss-Seidel-SR based hybrid algorithm as well as both classical Jacobi-SR method and Gauss-Seidel-SR method in terms of convergence speed and effectiveness.