OpenRaiser/Intern-Atlas huggingface dataset:
An Axiomatic Framework for Belief Updates
Venue: arXiv cs.AI 2013
Year: 2013

In the 1940's, a physicist named Cox provided the first formal justification for the axioms of probability based on the subjective or Bayesian interpretation. He showed that if a measure of belief satisfies several fundamental properties, then the measure must be some monotonic transformation of a probability. In this paper, measures of change in belief or belief updates are examined. In the spirit of Cox, properties for a measure of change in belief are enumerated. It is shown that if a measure satisfies these properties, it must satisfy other restrictive conditions. For example, it is shown that belief updates in a probabilistic context must be equal to some monotonic transformation of a likelihood ratio. It is hoped that this formal explication of the belief update paradigm will facilitate critical discussion and useful extensions of the approach.
Decision Under Uncertainty in Diagnosis
Venue: arXiv cs.AI 2013
Year: 2013

This paper describes the incorporation of uncertainty in diagnostic reasoning based on the set covering model of Reggia et. al. extended to what in the Artificial Intelligence dichotomy between deep and compiled (shallow, surface) knowledge based diagnosis may be viewed as the generic form at the compiled end of the spectrum. A major undercurrent in this is advocating the need for a strong underlying model and an integrated set of support tools for carrying such a model in order to deal with uncertainty.
Evidential Reasoning in Parallel Hierarchical Vision Programs
Venue: arXiv cs.AI 2013
Year: 2013

This paper presents an efficient adaptation and application of the Dempster-Shafer theory of evidence, one that can be used effectively in a massively parallel hierarchical system for visual pattern perception. It describes the techniques used, and shows in an extended example how they serve to improve the system's performance as it applies a multiple-level set of processes.
The Rational and Computational Scope of Probabilistic Rule-Based Expert Systems
Venue: arXiv cs.AI 2013
Year: 2013

Belief updating schemes in artificial intelligence may be viewed as three dimensional languages, consisting of a syntax (e.g. probabilities or certainty factors), a calculus (e.g. Bayesian or CF combination rules), and a semantics (i.e. cognitive interpretations of competing formalisms). This paper studies the rational scope of those languages on the syntax and calculus grounds. In particular, the paper presents an endomorphism theorem which highlights the limitations imposed by the conditional independence assumptions implicit in the CF calculus. Implications of the theorem to the relationship between the CF and the Bayesian languages and the Dempster-Shafer theory of evidence are presented. The paper concludes with a discussion of some implications on rule-based knowledge engineering in uncertain domains.
Appropriate and Inappropriate Estimation Techniques
Venue: arXiv cs.AI 2013
Year: 2013

Mode {also called MAP} estimation, mean estimation and median estimation are examined here to determine when they can be safely used to derive {posterior) cost minimizing estimates. (These are all Bayes procedures, using the mode. mean. or median of the posterior distribution). It is found that modal estimation only returns cost minimizing estimates when the cost function is 0-t. If the cost function is a function of distance then mean estimation only returns cost minimizing estimates when the cost function is squared distance from the true value and median estimation only returns cost minimizing estimates when the cost function ts the distance from the true value. Results are presented on the goodness or modal estimation with non 0-t cost functions
A VLSI Design and Implementation for a Real-Time Approximate Reasoning
Venue: arXiv cs.AI 2013
Year: 2013

The role of inferencing with uncertainty is becoming more important in rule-based expert systems (ES), since knowledge given by a human expert is often uncertain or imprecise. We have succeeded in designing a VLSI chip which can perform an entire inference process based on fuzzy logic. The design of the VLSI fuzzy inference engine emphasizes simplicity, extensibility, and efficiency (operational speed and layout area). It is fabricated in 2.5 um CMOS technology. The inference engine consists of three major components; a rule set memory, an inference processor, and a controller. In this implementation, a rule set memory is realized by a read only memory (ROM). The controller consists of two counters. In the inference processor, one data path is laid out for each rule. The number of the inference rule can be increased adding more data paths to the inference processor. All rules are executed in parallel, but each rule is processed serially. The logical structure of fuzzy inference proposed in the current paper maps nicely onto the VLSI structure. A two-phase nonoverlapping clocking scheme is used. Timing tests indicate that the inference engine can operate at approximately 20.8 MHz. This translates to an execution speed of approximately 80,000 Fuzzy Logical Inferences Per Second (FLIPS), and indicates that the inference engine is suitable for a demanding real-time application. The potential applications include decision-making in the area of command and control for intelligent robot systems, process control, missile and aircraft guidance, and other high performance machines.
Sustainable Cooperative Coevolution with a Multi-Armed Bandit
Venue: arXiv cs.NE 2013
Year: 2013

This paper proposes a self-adaptation mechanism to manage the resources allocated to the different species comprising a cooperative coevolutionary algorithm. The proposed approach relies on a dynamic extension to the well-known multi-armed bandit framework. At each iteration, the dynamic multi-armed bandit makes a decision on which species to evolve for a generation, using the history of progress made by the different species to guide the decisions. We show experimentally, on a benchmark and a real-world problem, that evolving the different populations at different paces allows not only to identify solutions more rapidly, but also improves the capacity of cooperative coevolution to solve more complex problems.
Logical Probability Preferences
Venue: arXiv cs.AI 2013
Year: 2013

We present a unified logical framework for representing and reasoning about both probability quantitative and qualitative preferences in probability answer set programming, called probability answer set optimization programs. The proposed framework is vital to allow defining probability quantitative preferences over the possible outcomes of qualitative preferences. We show the application of probability answer set optimization programs to a variant of the well-known nurse restoring problem, called the nurse restoring with probability preferences problem. To the best of our knowledge, this development is the first to consider a logical framework for reasoning about probability quantitative preferences, in general, and reasoning about both probability quantitative and qualitative preferences in particular.
The Computational Complexity of Random Serial Dictatorship
Venue: arXiv cs.GT 2013
Year: 2013

In social choice settings with linear preferences, random dictatorship is known to be the only social decision scheme satisfying strategyproofness and ex post efficiency. When also allowing indifferences, random serial dictatorship (RSD) is a well-known generalization of random dictatorship that retains both properties. RSD has been particularly successful in the special domain of random assignment where indifferences are unavoidable. While executing RSD is obviously feasible, we show that computing the resulting probabilities is #P-complete and thus intractable, both in the context of voting and assignment.
Characterization of Downlink Transmit Power Control during Soft Handover in WCDMA Systems
Venue: arXiv cs.NI 2013
Year: 2013

This paper presents the characterization of power control in WCDMA systems. We know that CDMA is an interference limited system. It is shown that the unbalance scheme is reliable and successful for both 2-way and 3-way soft handover. Unbalance scheme minimizes interference and speed up the soft handover algorithm to support more users quickly. Furthermore it requires minimum time to make decision for proper power control in soft handover status.
Performances Evaluation of Enhanced Basic Time Space Priority combined with an AQM
Venue: arXiv cs.NI 2013
Year: 2013

Active Queue Management(AQM) is an efficient tool in the network to avoid saturation of the queue by warning the sender that the queue is almost full to reduce its speed before the queue is full. The buffer management schemes focus on space management, in the other hand scheduling priorities (focusing on time management) attempt to guarantee acceptable delay boundaries to applications for which it is important that delay is bounded. Combined mechanisms (time and space management) are possible and enable networks to improve the perceived quality for multimedia traffic at the end users. The key idea in this paper is to study the performance of a mechanism combining an AQM with a time-space priority scheme applied to multimedia flows transmitted to an end user in HSDPA network. The studied queue is shared by Real Time and Non Real Time packets. We propose a mathematical model using Poisson and MMPP processes to model the arrival of packets in the system. The performance parameters are analytically deducted for the Combined EB-TSP and compared to the case of Simple EB-TSP. Numerical results obtained show the positive impact of the AQM added to the EB-TSP on the performance parameters of NRT packets compared to the Simple EB-TSP.
Validated Real Time Middle Ware For Distributed Cyber Physical Systems Using HMM
Venue: arXiv cs.DC 2013
Year: 2013

Distributed Cyber Physical Systems designed for different scenario must be capable enough to perform in an efficient manner in every situation. Earlier approaches, such as CORBA, has performed but with different time constraints. Therefore, there was the need to design reconfigurable, robust, validated and consistent real time middle ware systems with end-to-end timing. In the DCPS-HMM we have proposed the processor efficiency and data validation which may proof crucial in implementing various distributed systems such as credit card systems or file transfer through network.
On the Average Number of Edges in Theta Graphs
Venue: arXiv cs.CG 2013
Year: 2013

Theta graphs are important geometric graphs that have many applications, including wireless networking, motion planning, real-time animation, and minimum-spanning tree construction. We give closed form expressions for the average degree of theta graphs of a homogeneous Poisson point process over the plane. We then show that essentially the same bounds---with vanishing error terms---hold for theta graphs of finite sets of points that are uniformly distributed in a square. Finally, we show that the number of edges in a theta graph of points uniformly distributed in a square is concentrated around its expected value.
Merging Satellite Measurements of Rainfall Using Multi-scale Imagery Technique
Venue: arXiv cs.CV 2013
Year: 2013

Several passive microwave satellites orbit the Earth and measure rainfall. These measurements have the advantage of almost full global coverage when compared to surface rain gauges. However, these satellites have low temporal revisit and missing data over some regions. Image fusion is a useful technique to fill in the gaps of one image (one satellite measurement) using another one. The proposed algorithm uses an iterative fusion scheme to integrate information from two satellite measurements. The algorithm is implemented on two datasets for 7 years of half-hourly data. The results show significant improvements in rain detection and rain intensity in the merged measurements.
Independence and Bayesian Updating Methods
Venue: arXiv cs.AI 2013
Year: 2013

Duda, Hart, and Nilsson have set forth a method for rule-based inference systems to use in updating the probabilities of hypotheses on the basis of multiple items of new evidence. Pednault, Zucker, and Muresan claimed to give conditions under which independence assumptions made by Duda et al. preclude updating-that is, prevent the evidence from altering the probabilities of the hypotheses. Glymour refutes Pednault et al.'s claim with a counterexample of a rather special form (one item of evidence is incompatible with all but one of the hypotheses); he raises, but leaves open, the question whether their result would be true with an added assumption to rule out such special cases. We show that their result does not hold even with the added assumption, but that it can nevertheless be largely salvaged. Namely, under the conditions assumed by Pednault et al., at most one of the items of evidence can alter the probability of any given hypothesis; thus, although updating is possible, multiple updating for any of the hypotheses is precluded.
Foundations of Probability Theory for AI - The Application of Algorithmic Probability to Problems in Artificial Intelligence
Venue: arXiv cs.AI 2013
Year: 2013

This paper covers two topics: first an introduction to Algorithmic Complexity Theory: how it defines probability, some of its characteristic properties and past successful applications. Second, we apply it to problems in A.I. - where it promises to give near optimum search procedures for two very broad classes of problems.
Metaprobability and Dempster-Shafer in Evidential Reasoning
Venue: arXiv cs.AI 2013
Year: 2013

Evidential reasoning in expert systems has often used ad-hoc uncertainty calculi. Although it is generally accepted that probability theory provides a firm theoretical foundation, researchers have found some problems with its use as a workable uncertainty calculus. Among these problems are representation of ignorance, consistency of probabilistic judgements, and adjustment of a priori judgements with experience. The application of metaprobability theory to evidential reasoning is a new approach to solving these problems. Metaprobability theory can be viewed as a way to provide soft or hard constraints on beliefs in much the same manner as the Dempster-Shafer theory provides constraints on probability masses on subsets of the state space. Thus, we use the Dempster-Shafer theory, an alternative theory of evidential reasoning to illuminate metaprobability theory as a theory of evidential reasoning. The goal of this paper is to compare how metaprobability theory and Dempster-Shafer theory handle the adjustment of beliefs with evidence with respect to a particular thought experiment. Sections 2 and 3 give brief descriptions of the metaprobability and Dempster-Shafer theories. Metaprobability theory deals with higher order probabilities applied to evidential reasoning. Dempster-Shafer theory is a generalization of probability theory which has evolved from a theory of upper and lower probabilities. Section 4 describes a thought experiment and the metaprobability and DempsterShafer analysis of the experiment. The thought experiment focuses on forming beliefs about a population with 6 types of members {1, 2, 3, 4, 5, 6}. A type is uniquely defined by the values of three features: A, B, C. That is, if the three features of one member of the population were known then its type could be ascertained. Each of the three features has two possible values, (e.g. A can be either "a0" or "al"). Beliefs are formed from evidence accrued from two sensors: sensor A, and sensor B. Each sensor senses the corresponding defining feature. Sensor A reports that half of its observations are "a0" and half the observations are 'al'. Sensor B reports that half of its observations are ``b0,' and half are "bl". Based on these two pieces of evidence, what should be the beliefs on the distribution of types in the population? Note that the third feature is not observed by any sensor.
Inductive Inference and the Representation of Uncertainty
Venue: arXiv cs.AI 2013
Year: 2013

The form and justification of inductive inference rules depend strongly on the representation of uncertainty. This paper examines one generic representation, namely, incomplete information. The notion can be formalized by presuming that the relevant probabilities in a decision problem are known only to the extent that they belong to a class K of probability distributions. The concept is a generalization of a frequent suggestion that uncertainty be represented by intervals or ranges on probabilities. To make the representation useful for decision making, an inductive rule can be formulated which determines, in a well-defined manner, a best approximation to the unknown probability, given the set K. In addition, the knowledge set notion entails a natural procedure for updating -- modifying the set K given new evidence. Several non-intuitive consequences of updating emphasize the differences between inference with complete and inference with incomplete information.
Machine Learning, Clustering, and Polymorphy
Venue: arXiv cs.AI 2013
Year: 2013

This paper describes a machine induction program (WITT) that attempts to model human categorization. Properties of categories to which human subjects are sensitive includes best or prototypical members, relative contrasts between putative categories, and polymorphy (neither necessary or sufficient features). This approach represents an alternative to usual Artificial Intelligence approaches to generalization and conceptual clustering which tend to focus on necessary and sufficient feature rules, equivalence classes, and simple search and match schemes. WITT is shown to be more consistent with human categorization while potentially including results produced by more traditional clustering schemes. Applications of this approach in the domains of expert systems and information retrieval are also discussed.
A Framework for Control Strategies in Uncertain Inference Networks
Venue: arXiv cs.AI 2013
Year: 2013

Control Strategies for hierarchical tree-like probabilistic inference networks are formulated and investigated. Strategies that utilize staged look-ahead and temporary focus on subgoals are formalized and refined using the Depth Vector concept that serves as a tool for defining the 'virtual tree' regarded by the control strategy. The concept is illustrated by four types of control strategies for three-level trees that are characterized according to their Depth Vector, and according to the way they consider intermediate nodes and the role that they let these nodes play. INFERENTI is a computerized inference system written in Prolog, which provides tools for exercising a variety of control strategies. The system also provides tools for simulating test data and for comparing the relative average performance under different strategies.
Confidence Factors, Empiricism and the Dempster-Shafer Theory of Evidence
Venue: arXiv cs.AI 2013
Year: 2013

The issue of confidence factors in Knowledge Based Systems has become increasingly important and Dempster-Shafer (DS) theory has become increasingly popular as a basis for these factors. This paper discusses the need for an empirical lnterpretatlon of any theory of confidence factors applied to Knowledge Based Systems and describes an empirical lnterpretatlon of DS theory suggesting that the theory has been extensively misinterpreted. For the essentially syntactic DS theory, a model is developed based on sample spaces, the traditional semantic model of probability theory. This model is used to show that, if belief functions are based on reasonably accurate sampling or observation of a sample space, then the beliefs and upper probabilities as computed according to DS theory cannot be interpreted as frequency ratios. Since many proposed applications of DS theory use belief functions in situations with statistically derived evidence (Wesley [1]) and seem to appeal to statistical intuition to provide an lnterpretatlon of the results as has Garvey [2], it may be argued that DS theory has often been misapplied.
Interval-Based Decisions for Reasoning Systems
Venue: arXiv cs.AI 2013
Year: 2013

This essay looks at decision-making with interval-valued probability measures. Existing decision methods have either supplemented expected utility methods with additional criteria of optimality, or have attempted to supplement the interval-valued measures. We advocate a new approach, which makes the following questions moot: 1. which additional criteria to use, and 2. how wide intervals should be. In order to implement the approach, we need more epistemological information. Such information can be generated by a rule of acceptance with a parameter that allows various attitudes toward error, or can simply be declared. In sketch, the argument is: 1. probability intervals are useful and natural in All. systems; 2. wide intervals avoid error, but are useless in some risk sensitive decision-making; 3. one may obtain narrower intervals if one is less cautious; 4. if bodies of knowledge can be ordered by their caution, one should perform the decision analysis with the acceptable body of knowledge that is the most cautious, of those that are useful. The resulting behavior differs from that of a behavioral probabilist (a Bayesian) because in the proposal, 5. intervals based on successive bodies of knowledge are not always nested; 6. if the agent uses a probability for a particular decision, she need not commit to that probability for credence or future decision; and 7. there may be no acceptable body of knowledge that is useful; hence, sometimes no decision is mandated.
Machine Generalization and Human Categorization: An Information-Theoretic View
Venue: arXiv cs.AI 2013
Year: 2013

In designing an intelligent system that must be able to explain its reasoning to a human user, or to provide generalizations that the human user finds reasonable, it may be useful to take into consideration psychological data on what types of concepts and categories people naturally use. The psychological literature on concept learning and categorization provides strong evidence that certain categories are more easily learned, recalled, and recognized than others. We show here how a measure of the informational value of a category predicts the results of several important categorization experiments better than standard alternative explanations. This suggests that information-based approaches to machine generalization may prove particularly useful and natural for human users of the systems.