OpenRaiser/Intern-Atlas huggingface dataset:
Industrial-Strength Model-Based Testing - State of the Art and Current Challenges
Venue: arXiv cs.SE 2013
Year: 2013

As of today, model-based testing (MBT) is considered as leading-edge technology in industry. We sketch the different MBT variants that - according to our experience - are currently applied in practice, with special emphasis on the avionic, railway and automotive domains. The key factors for successful industrial-scale application of MBT are described, both from a scientific and a managerial point of view. With respect to the former view, we describe the techniques for automated test case, test data and test procedure generation for concurrent reactive real-time systems which are considered as the most important enablers for MBT in practice. With respect to the latter view, our experience with introducing MBT approaches in testing teams are sketched. Finally, the most challenging open scientific problems whose solutions are bound to improve the acceptance and effectiveness of MBT in industry are discussed.
Toward the Integration of Traditional and Agile Approaches
Venue: arXiv cs.SE 2013
Year: 2013

The agile approach uses continuous delivery, instead of distinct procedure, to work closer with customers and to respond faster requirement changes. All of these are against the traditional plan driven approach. Due to agile method's characteristics and its success in the real world practices, a number of discussions regarding the differences between agile and traditional approaches emerged recently and many studies intended to integrate both methods to synthesize the benefits from these two sides. However, this type of research often concludes from observations of a development activity or surveys after a project. To provide a more objective supportive evidence of comparing these two approaches, our research analyzes the source codes, logs, and notes. We argue that the agile and traditional approaches share common characteristics, which can be considered as the glue for integrating both methods. In our study, we collect all the submissions from the version control repository, and meeting notes and discussions. By applying our suggested analysis method, we illustrate the shared properties between agile and traditional approaches; thus, different development phases, like implementation and test, can still be identified in agile development history. This result not only provides a positive result for our hypothesis but also offers a suggestion for a better integration.
Large-Margin Metric Learning for Partitioning Problems
Venue: arXiv cs.LG 2013
Year: 2013

In this paper, we consider unsupervised partitioning problems, such as clustering, image segmentation, video segmentation and other change-point detection problems. We focus on partitioning problems based explicitly or implicitly on the minimization of Euclidean distortions, which include mean-based change-point detection, K-means, spectral clustering and normalized cuts. Our main goal is to learn a Mahalanobis metric for these unsupervised problems, leading to feature weighting and/or selection. This is done in a supervised way by assuming the availability of several potentially partially labelled datasets that share the same metric. We cast the metric learning problem as a large-margin structured prediction problem, with proper definition of regularizers and losses, leading to a convex optimization problem which can be solved efficiently with iterative techniques. We provide experiments where we show how learning the metric may significantly improve the partitioning performance in synthetic examples, bioinformatics, video segmentation and image segmentation problems.
Bandlimited Signal Reconstruction From the Distribution of Unknown Sampling Locations
Venue: arXiv cs.IT 2013
Year: 2013

We study the reconstruction of bandlimited fields from samples taken at unknown but statistically distributed sampling locations. The setup is motivated by distributed sampling where precise knowledge of sensor locations can be difficult. Periodic one-dimensional bandlimited fields are considered for sampling. Perfect samples of the field at independent and identically distributed locations are obtained. The statistical realization of sampling locations is not known. First, it is shown that a bandlimited field cannot be uniquely determined with samples taken at statistically distributed but unknown locations, even if the number of samples is infinite. Next, it is assumed that the order of sample locations is known. In this case, using insights from order-statistics, an estimate for the field with useful asymptotic properties is designed. Distortion (mean-squared error) and central-limit are established for this estimate.
Constant Unary Constraints and Symmetric Real-Weighted Counting Constraint Satisfaction Problems
Venue: arXiv cs.CC 2013
Year: 2013

A unary constraint (on the Boolean domain) is a function from {0,1} to the set of real numbers. A free use of auxiliary unary constraints given besides input instances has proven to be useful in establishing a complete classification of the computational complexity of approximately solving weighted counting Boolean constraint satisfaction problems (or #CSPs). In particular, two special constant unary constraints are a key to an arity reduction of arbitrary constraints, sufficient for the desired classification. In an exact counting model, both constant unary constraints are always assumed to be available since they can be eliminated efficiently using an arbitrary nonempty set of constraints. In contrast, we demonstrate in an approximate counting model, that at least one of them is efficiently approximated and thus eliminated approximately by a nonempty constraint set. This fact directly leads to an efficient construction of polynomial-time randomized approximation-preserving Turing reductions (or AP-reductions) from #CSPs with designated constraints to any given #CSPs composed of symmetric real-valued constraints of arbitrary arities even in the presence of arbitrary extra unary constraints.
From Conditional Oughts to Qualitative Decision Theory
Venue: arXiv cs.AI 2013
Year: 2013

The primary theme of this investigation is a decision theoretic account of conditional ought statements (e.g., "You ought to do A, if C") that rectifies glaring deficiencies in classical deontic logic. The resulting account forms a sound basis for qualitative decision theory, thus providing a framework for qualitative planning under uncertainty. In particular, we show that adding causal relationships (in the form of a single graph) as part of an epistemic state is sufficient to facilitate the analysis of action sequences, their consequences, their interaction with observations, their expected utilities and, hence, the synthesis of plans and strategies under uncertainty.
A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification
Venue: arXiv cs.AI 2013
Year: 2013

The problems associated with scaling involve active and challenging research topics in the area of artificial intelligence. The purpose is to solve real world problems by means of AI technologies, in cases where the complexity of representation of the real world problem is potentially combinatorial. In this paper, we present a novel approach to cope with the scaling issues in Bayesian belief networks for ship classification. The proposed approach divides the conceptual model of a complex ship classification problem into a set of small modules that work together to solve the classification problem while preserving the functionality of the original model. The possible ways of explaining sensor returns (e.g., the evidence) for some features, such as portholes along the length of a ship, are sometimes combinatorial. Thus, using an exhaustive approach, which entails the enumeration of all possible explanations, is impractical for larger problems. We present a network structure (referred to as Sequential Decomposition, SD) in which each observation is associated with a set of legitimate outcomes which are consistent with the explanation of each observed piece of evidence. The results show that the SD approach allows one to represent feature-observation relations in a manageable way and achieve the same explanatory power as an exhaustive approach.
Normative Engineering Risk Management Systems
Venue: arXiv cs.AI 2013
Year: 2013

This paper describes a normative system design that incorporates diagnosis, dynamic evolution, decision making, and information gathering. A single influence diagram demonstrates the design's coherence, yet each activity is more effectively modeled and evaluated separately. Application to offshore oil platforms illustrates the design. For this application, the normative system is embedded in a real-time expert system.
Parameter Adjustment in Bayes Networks. The generalized noisy OR-gate
Venue: arXiv cs.AI 2013
Year: 2013

Spiegelhalter and Lauritzen [15] studied sequential learning in Bayesian networks and proposed three models for the representation of conditional probabilities. A forth model, shown here, assumes that the parameter distribution is given by a product of Gaussian functions and updates them from the _ and _r messages of evidence propagation. We also generalize the noisy OR-gate for multivalued variables, develop the algorithm to compute probability in time proportional to the number of parents (even in networks with loops) and apply the learning model to this gate.
Dialectic Reasoning with Inconsistent Information
Venue: arXiv cs.AI 2013
Year: 2013

From an inconsistent database non-trivial arguments may be constructed both for a proposition, and for the contrary of that proposition. Therefore, inconsistency in a logical database causes uncertainty about which conclusions to accept. This kind of uncertainty is called logical uncertainty. We define a concept of "acceptability", which induces a means for differentiating arguments. The more acceptable an argument, the more confident we are in it. A specific interest is to use the acceptability classes to assign linguistic qualifiers to propositions, such that the qualifier assigned to a propositions reflects its logical uncertainty. A more general interest is to understand how classes of acceptability can be defined for arguments constructed from an inconsistent database, and how this notion of acceptability can be devised to reflect different criteria. Whilst concentrating on the aspects of assigning linguistic qualifiers to propositions, we also indicate the more general significance of the notion of acceptability.
Utility-Based Abstraction and Categorization
Venue: arXiv cs.AI 2013
Year: 2013

We take a utility-based approach to categorization. We construct generalizations about events and actions by considering losses associated with failing to distinguish among detailed distinctions in a decision model. The utility-based methods transform detailed states of the world into more abstract categories comprised of disjunctions of the states. We show how we can cluster distinctions into groups of distinctions at progressively higher levels of abstraction, and describe rules for decision making with the abstractions. The techniques introduce a utility-based perspective on the nature of concepts, and provide a means of simplifying decision models used in automated reasoning systems. We demonstrate the techniques by describing the capabilities and output of TUBA, a program for utility-based abstraction.
Sensitivity Analysis for Probability Assessments in Bayesian Networks
Venue: arXiv cs.AI 2013
Year: 2013

When eliciting probability models from experts, knowledge engineers may compare the results of the model with expert judgment on test scenarios, then adjust model parameters to bring the behavior of the model more in line with the expert's intuition. This paper presents a methodology for analytic computation of sensitivity values to measure the impact of small changes in a network parameter on a target probability value or distribution. These values can be used to guide knowledge elicitation. They can also be used in a gradient descent algorithm to estimate parameter values that maximize a measure of goodness-of-fit to both local and holistic probability assessments.
Some Complexity Considerations in the Combination of Belief Networks
Venue: arXiv cs.AI 2013
Year: 2013

One topic that is likely to attract an increasing amount of attention within the Knowledge-base systems research community is the coordination of information provided by multiple experts. We envision a situation in which several experts independently encode information as belief networks. A potential user must then coordinate the conclusions and recommendations of these networks to derive some sort of consensus. One approach to such a consensus is the fusion of the contributed networks into a single, consensus model prior to the consideration of any case-specific data (specific observations, test results). This approach requires two types of combination procedures, one for probabilities, and one for graphs. Since the combination of probabilities is relatively well understood, the key barriers to this approach lie in the realm of graph theory. This paper provides formal definitions of some of the operations necessary to effect the necessary graphical combinations, and provides complexity analyses of these procedures. The paper's key result is that most of these operations are NP-hard, and its primary message is that the derivation of ?good? consensus networks must be done heuristically.
Valuation Networks and Conditional Independence
Venue: arXiv cs.AI 2013
Year: 2013

Valuation networks have been proposed as graphical representations of valuation-based systems (VBSs). The VBS framework is able to capture many uncertainty calculi including probability theory, Dempster-Shafer's belief-function theory, Spohn's epistemic belief theory, and Zadeh's possibility theory. In this paper, we show how valuation networks encode conditional independence relations. For the probabilistic case, the class of probability models encoded by valuation networks includes undirected graph models, directed acyclic graph models, directed balloon graph models, and recursive causal graph models.
Relevant Explanations: Allowing Disjunctive Assignments
Venue: arXiv cs.AI 2013
Year: 2013

Relevance-based explanation is a scheme in which partial assignments to Bayesian belief network variables are explanations (abductive conclusions). We allow variables to remain unassigned in explanations as long as they are irrelevant to the explanation, where irrelevance is defined in terms of statistical independence. When multiple-valued variables exist in the system, especially when subsets of values correspond to natural types of events, the over specification problem, alleviated by independence-based explanation, resurfaces. As a solution to that, as well as for addressing the question of explanation specificity, it is desirable to collapse such a subset of values into a single value on the fly. The equivalent method, which is adopted here, is to generalize the notion of assignments to allow disjunctive assignments. We proceed to define generalized independence based explanations as maximum posterior probability independence based generalized assignments (GIB-MAPs). GIB assignments are shown to have certain properties that ease the design of algorithms for computing GIB-MAPs. One such algorithm is discussed here, as well as suggestions for how other algorithms may be adapted to compute GIB-MAPs. GIB-MAP explanations still suffer from instability, a problem which may be addressed using ?approximate? conditional independence as a condition for irrelevance.
Representing and Reasoning With Probabilistic Knowledge: A Bayesian Approach
Venue: arXiv cs.AI 2013
Year: 2013

PAGODA (Probabilistic Autonomous Goal-Directed Agent) is a model for autonomous learning in probabilistic domains [desJardins, 1992] that incorporates innovative techniques for using the agent's existing knowledge to guide and constrain the learning process and for representing, reasoning with, and learning probabilistic knowledge. This paper describes the probabilistic representation and inference mechanism used in PAGODA. PAGODA forms theories about the effects of its actions and the world state on the environment over time. These theories are represented as conditional probability distributions. A restriction is imposed on the structure of the theories that allows the inference mechanism to find a unique predicted distribution for any action and world state description. These restricted theories are called uniquely predictive theories. The inference mechanism, Probability Combination using Independence (PCI), uses minimal independence assumptions to combine the probabilities in a theory to make probabilistic predictions.
Using Causal Information and Local Measures to Learn Bayesian Networks
Venue: arXiv cs.AI 2013
Year: 2013

In previous work we developed a method of learning Bayesian Network models from raw data. This method relies on the well known minimal description length (MDL) principle. The MDL principle is particularly well suited to this task as it allows us to tradeoff, in a principled way, the accuracy of the learned network against its practical usefulness. In this paper we present some new results that have arisen from our work. In particular, we present a new local way of computing the description length. This allows us to make significant improvements in our search algorithm. In addition, we modify our algorithm so that it can take into account partial domain information that might be provided by a domain expert. The local computation of description length also opens the door for local refinement of an existent network. The feasibility of our approach is demonstrated by experiments involving networks of a practical size.
Knowledge-Based Decision Model Construction for Hierarchical Diagnosis: A Preliminary Report
Venue: arXiv cs.AI 2013
Year: 2013

Numerous methods for probabilistic reasoning in large, complex belief or decision networks are currently being developed. There has been little research on automating the dynamic, incremental construction of decision models. A uniform value-driven method of decision model construction is proposed for the hierarchical complete diagnosis. Hierarchical complete diagnostic reasoning is formulated as a stochastic process and modeled using influence diagrams. Given observations, this method creates decision models in order to obtain the best actions sequentially for locating and repairing a fault at minimum cost. This method construct decision models incrementally, interleaving probe actions with model construction and evaluation. The method treats meta-level and baselevel tasks uniformly. That is, the method takes a decision-theoretic look at the control of search in causal pathways and structural hierarchies.
A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging
Venue: arXiv cs.AI 2013
Year: 2013

We describe the integration of logical and uncertain reasoning methods to identify the likely source and location of software problems. To date, software engineers have had few tools for identifying the sources of error in complex software packages. We describe a method for diagnosing software problems through combining logical and uncertain reasoning analyses. Our preliminary results suggest that such methods can be of value in directing the attention of software engineers to paths of an algorithm that have the highest likelihood of harboring a programming error.
An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks
Venue: arXiv cs.AI 2013
Year: 2013

In recent years the belief network has been used increasingly to model systems in Al that must perform uncertain inference. The development of efficient algorithms for probabilistic inference in belief networks has been a focus of much research in AI. Efficient algorithms for certain classes of belief networks have been developed, but the problem of reporting the uncertainty in inferred probabilities has received little attention. A system should not only be capable of reporting the values of inferred probabilities and/or the favorable choices of a decision; it should report the range of possible error in the inferred probabilities and/or choices. Two methods have been developed and implemented for determining the variance in inferred probabilities in belief networks. These methods, the Approximate Propagation Method and the Monte Carlo Integration Method are discussed and compared in this paper.
Deliberation Scheduling for Time-Critical Sequential Decision Making
Venue: arXiv cs.AI 2013
Year: 2013

We describe a method for time-critical decision making involving sequential tasks and stochastic processes. The method employs several iterative refinement routines for solving different aspects of the decision making problem. This paper concentrates on the meta-level control problem of deliberation scheduling, allocating computational resources to these routines. We provide different models corresponding to optimization problems that capture the different circumstances and computational strategies for decision making under time constraints. We consider precursor models in which all decision making is performed prior to execution and recurrent models in which decision making is performed in parallel with execution, accounting for the states observed during execution and anticipating future states. We describe algorithms for precursor and recurrent models and provide the results of our empirical investigations to date.
A Method for Planning Given Uncertain and Incomplete Information
Venue: arXiv cs.AI 2013
Year: 2013

This paper describes ongoing research into planning in an uncertain environment. In particular, it introduces U-Plan, a planning system that constructs quantitatively ranked plans given an incomplete description of the state of the world. U-Plan uses a DempsterShafer interval to characterise uncertain and incomplete information about the state of the world. The planner takes as input what is known about the world, and constructs a number of possible initial states with representations at different abstraction levels. A plan is constructed for the initial state with the greatest support, and this plan is tested to see if it will work for other possible initial states. All, part, or none of the existing plans may be used in the generation of the plans for the remaining possible worlds. Planning takes place in an abstraction hierarchy where strategic decisions are made before tactical decisions. A super-plan is then constructed, based on merging the set of plans and the appropriately timed acquisition of essential knowledge, which is used to decide between plan alternatives. U-Plan usually produces a super-plan in less time than a classical planner would take to produce a set of plans, one for each possible world.
The use of conflicts in searching Bayesian networks
Venue: arXiv cs.AI 2013
Year: 2013

This paper discusses how conflicts (as used by the consistency-based diagnosis community) can be adapted to be used in a search-based algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime" algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular Bayesian net algorithms exploit the structure of the network for efficiency, we exploit probability distributions for efficiency; this algorithm is most suited to the case with extreme probabilities. This paper presents a solution to the inefficiencies found in naive algorithms, and shows how the tools of the consistency-based diagnosis community (namely conflicts) can be used effectively to improve the efficiency. Empirical results with networks having tens of thousands of nodes are presented.