bluuebunny/crossref_metadata_2025 huggingface dataset:
Answer Sets for Logic Programs with Arbitrary Abstract Constraint Atoms
Authors: Son T. C. , Pontelli E. , Tu P. H.
Published: July 2018

In this paper, we present two alternative approaches to defining answer sets for logic programs with arbitrary types of abstract constraint atoms (c-atoms). These approaches generalize the fixpoint-based and the level mapping based answer set semantics of normal logic programs to the case of logic programs with arbitrary types of c-atoms. The results are four different answer set definitions which are equivalent when applied to normal logic programs.

The standard fixpoint-based semantics of logic programs is generalized in two directions, called answer set by reduct and answer set by complement. These definitions, which differ from each other in the treatment of negation-as-failure (naf) atoms, make use of an immediate consequence operator to perform answer set checking, whose definition relies on the notion of conditional satisfaction of c-atoms w.r.t. a pair of interpretations.

The other two definitions, called strongly and weakly well-supported models, are generalizations of the notion of well-supported models of normal logic programs to the case of programs with c-atoms. As for the case of fixpoint-based semantics, the difference between these two definitions is rooted in the treatment of naf atoms.

We prove that answer sets by reduct (resp. by complement) are equivalent to weakly (resp. strongly) well-supported models of a program, thus generalizing the theorem on the correspondence between stable models and well-supported models of a normal logic program to the class of programs with c-atoms.

We show that the newly defined semantics coincide with previously introduced semantics for logic programs with monotone c-atoms, and they extend the original answer set semantics of normal logic programs. We also study some properties of answer sets of programs with c-atoms, and relate our definitions to several semantics for logic programs with aggregates presented in the literature.
Learning Semantic Definitions of Online Information Sources
Authors: Carman M. J. , Knoblock C. A.
Published: July 2018

The Internet contains a very large number of information sources providing many types of data from weather forecasts to travel deals and financial information. These sources can be accessed via Web-forms, Web Services, RSS feeds and so on. In order to make automated use of these sources, we need to model them semantically, but writing semantic descriptions for Web Services is both tedious and error prone. In this paper we investigate the problem of automatically generating such models. We introduce a framework for learning Datalog definitions of Web sources. In order to learn these definitions, our system actively invokes the sources and compares the data they produce with that of known sources of information. It then performs an inductive logic search through the space of plausible source definitions in order to learn the best possible semantic model for each new source. In this paper we perform an empirical evaluation of the system using real-world Web sources. The evaluation demonstrates the effectiveness of the approach, showing that we can automatically learn complex models for real sources in reasonable time. We also compare our system with a complex schema matching system, showing that our approach can handle the kinds of problems tackled by the latter.
New Inference Rules for Max-SAT
Authors: Li C. M. , Manya F. , Planes J.
Published: July 2018

Exact Max-SAT solvers, compared with SAT solvers, apply little inference at each node of the proof tree. Commonly used SAT inference rules like unit propagation produce a simplified formula that preserves satisfiability but, unfortunately, solving the Max-SAT problem for the simplified formula is not equivalent to solving it for the original formula. In this paper, we define a number of original inference rules that, besides being applied efficiently, transform Max-SAT instances into equivalent Max-SAT instances which are easier to solve. The soundness of the rules, that can be seen as refinements of unit resolution adapted to Max-SAT, are proved in a novel and simple way via an integer programming transformation. With the aim of finding out how powerful the inference rules are in practice, we have developed a new Max-SAT solver, called MaxSatz, which incorporates those rules, and performed an experimental investigation. The results provide empirical evidence that MaxSatz is very competitive, at least, on random Max-2SAT, random Max-3SAT, Max-Cut, and Graph 3-coloring instances, as well as on the benchmarks from the Max-SAT Evaluation 2006.
On the Formal Semantics of Speech-Act Based Communication in an Agent-Oriented Programming Language
Authors: Vieira R. , Moreira A. F. , Wooldridge M. , Bordini R. H.
Published: July 2018

Research on agent communication languages has typically taken the speech acts paradigm as its starting point. Despite their manifest attractions, speech-act models of communication have several serious disadvantages as a foundation for communication in artificial agent systems. In particular, it has proved to be extremely difficult to give a satisfactory semantics to speech-act based agent communication languages. In part, the problem is that speech-act semantics typically make reference to the "mental states" of agents (their beliefs, desires, and intentions), and there is in general no way to attribute such attitudes to arbitrary computational agents. In addition, agent programming languages have only had their semantics formalised for abstract, stand-alone versions, neglecting aspects such as communication primitives. With respect to communication, implemented agent programming languages have tended to be rather ad hoc. This paper addresses both of these problems, by giving semantics to speech-act based messages received by an AgentSpeak agent. AgentSpeak is a logic-based agent programming language which incorporates the main features of the PRS model of reactive planning systems. The paper builds upon a structural operational semantics to AgentSpeak that we developed in previous work. The main contributions of this paper are as follows: an extension of our earlier work on the theoretical foundations of AgentSpeak interpreters; a computationally grounded semantics for (the core) performatives used in speech-act based agent communication languages; and a well-defined extension of AgentSpeak that supports agent communication.
A Framework for Kernel-Based Multi-Category Classification
Authors: Hill S. I. , Doucet A.
Published: July 2018

A geometric framework for understanding multi-category classification is introduced, through which many existing 'all-together' algorithms can be understood. The structure enables parsimonious optimisation, through a direct extension of the binary methodology. The focus is on Support Vector Classification, with parallels drawn to related methods.

The ability of the framework to compare algorithms is illustrated by a brief discussion of Fisher consistency. Its utility in improving understanding of multi-category analysis is demonstrated through a derivation of improved generalisation bounds.

It is also described how this architecture provides insights regarding how to further improve on the speed of existing multi-category classification algorithms. An initial example of how this might be achieved is developed in the formulation of a straightforward multi-category Sequential Minimal Optimisation algorithm. Proof-of-concept experimental results have shown that this, combined with the mapping of pairwise results, is comparable with benchmark optimisation speeds.
Knowledge Derived From Wikipedia For Computing Semantic Relatedness
Authors: Ponzetto S. P. , Strube M.
Published: July 2018

Wikipedia provides a semantic network for computing semantic relatedness in a more structured fashion than a search engine and with more coverage than WordNet. We present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet on some datasets. We also address the question whether and how Wikipedia can be integrated into NLP applications as a knowledge base. Including Wikipedia improves the performance of a machine learning based coreference resolution system, indicating that it represents a valuable resource for NLP applications. Finally, we show that our method can be easily used for languages other than English by computing semantic relatedness for a German dataset.
Natural Events
Authors: Bell J.
Published: July 2018

This paper develops an inductive theory of predictive common sense reasoning. The theory provides the basis for an integrated solution to the three traditional problems of reasoning about change; the frame, qualification, and ramification problems. The theory is also capable of representing non-deterministic events, and it provides a means for stating defeasible preferences over the outcomes of conflicting simultaneous events.
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
Authors: Ruiz A. , Lopez-de-Teruel P. E. , Garrido M. C.
Published: July 2018

This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of finite mixture models, conjugate families and factorization. Both the joint probability density of the variables and the likelihood function of the (objective or subjective) observation are approximated by a special mixture model, in such a way that any desired conditional distribution can be directly obtained without numerical integration. We have developed an extended version of the expectation maximization (EM) algorithm to estimate the parameters of mixture models from uncertain training examples (indirect observations). As a consequence, any piece of exact or uncertain information about both input and output values is consistently handled in the inference and learning stages. This ability, extremely useful in certain situations, is not found in most alternative methods. The proposed framework is formally justified from standard probabilistic principles and illustrative examples are provided in the fields of nonparametric pattern classification, nonlinear regression and pattern completion. Finally, experiments on a real application and comparative results over standard databases provide empirical evidence of the utility of the method in a wide range of applications.
Complexity of Prioritized Default Logics
Authors: Rintanen J.
Published: July 2018

In default reasoning, usually not all possible ways of resolving conflicts between default rules are acceptable. Criteria expressing acceptable ways of resolving the conflicts may be hardwired in the inference mechanism, for example specificity in inheritance reasoning can be handled this way, or they may be given abstractly as an ordering on the default rules. In this article we investigate formalizations of the latter approach in Reiter's default logic. Our goal is to analyze and compare the computational properties of three such formalizations in terms of their computational complexity: the prioritized default logics of Baader and Hollunder, and Brewka, and a prioritized default logic that is based on lexicographic comparison. The analysis locates the propositional variants of these logics on the second and third levels of the polynomial hierarchy, and identifies the boundary between tractable and intractable inference for restricted classes of prioritized default theories.
Efficient Implementation of the Plan Graph in STAN
Authors: Long D. , Fox M.
Published: July 2018

STAN is a Graphplan-based planner, so-called because it uses a variety of STate ANalysis techniques to enhance its performance. STAN competed in the AIPS-98 planning competition where it compared well with the other competitors in terms of speed, finding solutions fastest to many of the problems posed. Although the domain analysis techniques STAN exploits are an important factor in its overall performance, we believe that the speed at which STAN solved the competition problems is largely due to the implementation of its plan graph. The implementation is based on two insights: that many of the graph construction operations can be implemented as bit-level logical operations on bit vectors, and that the graph should not be explicitly constructed beyond the fix point. This paper describes the implementation of STAN's plan graph and provides experimental results which demonstrate the circumstances under which advantages can be obtained from using this implementation.
Issues in Stacked Generalization
Authors: Ting K. M. , Witten I. H.
Published: July 2018

Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a `black art' in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We find that best results are obtained when the higher-level model combines the confidence (and not just the predictions) of the lower-level ones. We demonstrate the effectiveness of stacked generalization for combining three different types of learning algorithms for classification tasks. We also compare the performance of stacked generalization with majority vote and published results of arcing and bagging.
Randomized Algorithms for the Loop Cutset Problem
Authors: Becker A. , Bar-Yehuda R. , Geiger D.
Published: July 2018

We show how to find a minimum weight loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in the method of conditioning for inference. Our randomized algorithm for finding a loop cutset outputs a minimum loop cutset after O(c 6^k kn) steps with probability at least 1 - (1 - 1/(6^k))^c6^k, where c > 1 is a constant specified by the user, k is the minimal size of a minimum weight loop cutset, and n is the number of vertices. We also show empirically that a variant of this algorithm often finds a loop cutset that is closer to the minimum weight loop cutset than the ones found by the best deterministic algorithms known.
OBDD-based Universal Planning for Synchronized Agents in Non-Deterministic Domains
Authors: Jensen R. M. , Veloso M. M.
Published: July 2018

Recently model checking representation and search techniques were shown to be efficiently applicable to planning, in particular to non-deterministic planning. Such planning approaches use Ordered Binary Decision Diagrams (OBDDs) to encode a planning domain as a non-deterministic finite automaton and then apply fast algorithms from model checking to search for a solution. OBDDs can effectively scale and can provide universal plans for complex planning domains. We are particularly interested in addressing the complexities arising in non-deterministic, multi-agent domains. In this article, we present UMOP, a new universal OBDD-based planning framework for non-deterministic, multi-agent domains. We introduce a new planning domain description language, NADL, to specify non-deterministic, multi-agent domains. The language contributes the explicit definition of controllable agents and uncontrollable environment agents. We describe the syntax and semantics of NADL and show how to build an efficient OBDD-based representation of an NADL description. The UMOP planning system uses NADL and different OBDD-based universal planning algorithms. It includes the previously developed strong and strong cyclic planning algorithms. In addition, we introduce our new optimistic planning algorithm that relaxes optimality guarantees and generates plausible universal plans in some domains where no strong nor strong cyclic solution exists. We present empirical results applying UMOP to domains ranging from deterministic and single-agent with no environment actions to non-deterministic and multi-agent with complex environment actions. UMOP is shown to be a rich and efficient planning system.
Space Efficiency of Propositional Knowledge Representation Formalisms
Authors: Cadoli M. , Donini F. M. , Liberatore P. , Schaerf M.
Published: July 2018

We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Intuitively, the space efficiency of a formalism F in representing a certain piece of knowledge A, is the size of the shortest formula of F that represents A. In this paper we assume that knowledge is either a set of propositional interpretations (models) or a set of propositional formulae (theorems). We provide a formal way of talking about the relative ability of PKR formalisms to compactly represent a set of models or a set of theorems. We introduce two new compactness measures, the corresponding classes, and show that the relative space efficiency of a PKR formalism in representing models/theorems is directly related to such classes. In particular, we consider formalisms for nonmonotonic reasoning, such as circumscription and default logic, as well as belief revision operators and the stable model semantics for logic programs with negation. One interesting result is that formalisms with the same time complexity do not necessarily belong to the same space efficiency class.
Robust Agent Teams via Socially-Attentive Monitoring
Authors: Kaminka G. A. , Tambe M.
Published: July 2018

Agents in dynamic multi-agent environments must monitor their peers to execute individual and group plans. A key open question is how much monitoring of other agents' states is required to be effective: The Monitoring Selectivity Problem. We investigate this question in the context of detecting failures in teams of cooperating agents, via Socially-Attentive Monitoring, which focuses on monitoring for failures in the social relationships between the agents. We empirically and analytically explore a family of socially-attentive teamwork monitoring algorithms in two dynamic, complex, multi-agent domains, under varying conditions of task distribution and uncertainty. We show that a centralized scheme using a complex algorithm trades correctness for completeness and requires monitoring all teammates. In contrast, a simple distributed teamwork monitoring algorithm results in correct and complete detection of teamwork failures, despite relying on limited, uncertain knowledge, and monitoring only key agents in a team. In addition, we report on the design of a socially-attentive monitoring system and demonstrate its generality in monitoring several coordination relationships, diagnosing detected failures, and both on-line and off-line applications.
What's in an Attribute? Consequences for the Least Common Subsumer
Authors: Kusters R. , Borgida A.
Published: July 2018

Functional relationships between objects, called `attributes', are of considerable importance in knowledge representation languages, including Description Logics (DLs). A study of the literature indicates that papers have made, often implicitly, different assumptions about the nature of attributes: whether they are always required to have a value, or whether they can be partial functions. The work presented here is the first explicit study of this difference for subclasses of the CLASSIC DL, involving the same-as concept constructor. It is shown that although determining subsumption between concept descriptions has the same complexity (though requiring different algorithms), the story is different in the case of determining the least common subsumer (lcs). For attributes interpreted as partial functions, the lcs exists and can be computed relatively easily; even in this case our results correct and extend three previous papers about the lcs of DLs. In the case where attributes must have a value, the lcs may not exist, and even if it exists it may be of exponential size. Interestingly, it is possible to decide in polynomial time if the lcs exists.
The Complexity of Reasoning with Cardinality Restrictions and Nominals in Expressive Description Logics
Authors: Tobies S.
Published: July 2018

We study the complexity of the combination of the Description Logics ALCQ and ALCQI with a terminological formalism based on cardinality restrictions on concepts. These combinations can naturally be embedded into C^2, the two variable fragment of predicate logic with counting quantifiers, which yields decidability in NExpTime. We show that this approach leads to an optimal solution for ALCQI, as ALCQI with cardinality restrictions has the same complexity as C^2 (NExpTime-complete). In contrast, we show that for ALCQ, the problem can be solved in ExpTime. This result is obtained by a reduction of reasoning with cardinality restrictions to reasoning with the (in general weaker) terminological formalism of general axioms for ALCQ extended with nominals. Using the same reduction, we show that, for the extension of ALCQI with nominals, reasoning with general axioms is a NExpTime-complete problem. Finally, we sharpen this result and show that pure concept satisfiability for ALCQI with nominals is NExpTime-complete. Without nominals, this problem is known to be PSpace-complete.
An Application of Reinforcement Learning to Dialogue Strategy Selection in a Spoken Dialogue System for Email
Authors: Walker M. A.
Published: July 2018

This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method is based on a combination of reinforcement learning and performance modeling of spoken dialogue systems. The reinforcement learning component applies Q-learning (Watkins, 1989), while the performance modeling component applies the PARADISE evaluation framework (Walker et al., 1997) to learn the performance function (reward) used in reinforcement learning. We illustrate the method with a spoken dialogue system named ELVIS (EmaiL Voice Interactive System), that supports access to email over the phone. We conduct a set of experiments for training an optimal dialogue strategy on a corpus of 219 dialogues in which human users interact with ELVIS over the phone. We then test that strategy on a corpus of 18 dialogues. We show that ELVIS can learn to optimize its strategy selection for agent initiative, for reading messages, and for summarizing email folders.
Technical Paper Recommendation: A Study in Combining Multiple Information Sources
Authors: Basu C. , Hirsh H. , Cohen W. W. , Nevill-Manning C.
Published: July 2018

The growing need to manage and exploit the proliferation of online data sources is opening up new opportunities for bringing people closer to the resources they need. For instance, consider a recommendation service through which researchers can receive daily pointers to journal papers in their fields of interest. We survey some of the known approaches to the problem of technical paper recommendation and ask how they can be extended to deal with multiple information sources. More specifically, we focus on a variant of this problem -- recommending conference paper submissions to reviewing committee members -- which offers us a testbed to try different approaches. Using WHIRL -- an information integration system -- we are able to implement different recommendation algorithms derived from information retrieval principles. We also use a novel autonomous procedure for gathering reviewer interest information from the Web. We evaluate our approach and compare it to other methods using preference data provided by members of the AAAI-98 conference reviewing committee along with data about the actual submissions.
AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks
Authors: Cheng J. , Druzdzel M. J.
Published: July 2018

Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung & Chang, 1989; Shachter & Peot, 1989) and self-importance sampling (Shachter & Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, & Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, & Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.
Reasoning within Fuzzy Description Logics
Authors: Straccia U.
Published: July 2018

Description Logics (DLs) are suitable, well-known, logics for managing structured knowledge. They allow reasoning about individuals and well defined concepts, i.e., set of individuals with common properties. The experience in using DLs in applications has shown that in many cases we would like to extend their capabilities. In particular, their use in the context of Multimedia Information Retrieval (MIR) leads to the convincement that such DLs should allow the treatment of the inherent imprecision in multimedia object content representation and retrieval. In this paper we will present a fuzzy extension of ALC, combining Zadeh's fuzzy logic with a classical DL. In particular, concepts becomes fuzzy and, thus, reasoning about imprecise concepts is supported. We will define its syntax, its semantics, describe its properties and present a constraint propagation calculus for reasoning in it.
GIB: Imperfect Information in a Computationally Challenging Game
Authors: Ginsberg M. L.
Published: July 2018

This paper investigates the problems arising in the construction of a program to play the game of contract bridge. These problems include both the difficulty of solving the game's perfect information variant, and techniques needed to address the fact that bridge is not, in fact, a perfect information game. GIB, the program being described, involves five separate technical advances: partition search, the practical application of Monte Carlo techniques to realistic problems, a focus on achievable sets to solve problems inherent in the Monte Carlo approach, an extension of alpha-beta pruning from total orders to arbitrary distributive lattices, and the use of squeaky wheel optimization to find approximately optimal solutions to cardplay problems. GIB is currently believed to be of approximately expert caliber, and is currently the strongest computer bridge program in the world.
Domain Filtering Consistencies
Authors: Debruyne R. , Bessiere C.
Published: July 2018

Enforcing local consistencies is one of the main features of constraint reasoning. Which level of local consistency should be used when searching for solutions in a constraint network is a basic question. Arc consistency and partial forms of arc consistency have been widely studied, and have been known for sometime through the forward checking or the MAC search algorithms. Until recently, stronger forms of local consistency remained limited to those that change the structure of the constraint graph, and thus, could not be used in practice, especially on large networks. This paper focuses on the local consistencies that are stronger than arc consistency, without changing the structure of the network, i.e., only removing inconsistent values from the domains. In the last five years, several such local consistencies have been proposed by us or by others. We make an overview of all of them, and highlight some relations between them. We compare them both theoretically and experimentally, considering their pruning efficiency and the time required to enforce them.
ATTac-2000: An Adaptive Autonomous Bidding Agent
Authors: Stone P. , Littman M. L. , Singh S. , Kearns M.
Published: July 2018

The First Trading Agent Competition (TAC) was held from June 22nd to July 8th, 2000. TAC was designed to create a benchmark problem in the complex domain of e-marketplaces and to motivate researchers to apply unique approaches to a common task. This article describes ATTac-2000, the first-place finisher in TAC. ATTac-2000 uses a principled bidding strategy that includes several elements of adaptivity. In addition to the success at the competition, isolated empirical results are presented indicating the robustness and effectiveness of ATTac-2000's adaptive strategy.
Extensions of Simple Conceptual Graphs: the Complexity of Rules and Constraints
Authors: Baget J. F. , Mugnier M. L.
Published: July 2018

Simple conceptual graphs are considered as the kernel of most knowledge representation formalisms built upon Sowa's model. Reasoning in this model can be expressed by a graph homomorphism called projection, whose semantics is usually given in terms of positive, conjunctive, existential FOL. We present here a family of extensions of this model, based on rules and constraints, keeping graph homomorphism as the basic operation. We focus on the formal definitions of the different models obtained, including their operational semantics and relationships with FOL, and we analyze the decidability and complexity of the associated problems (consistency and deduction). As soon as rules are involved in reasonings, these problems are not decidable, but we exhibit a condition under which they fall in the polynomial hierarchy. These results extend and complete the ones already published by the authors. Moreover we systematically study the complexity of some particular cases obtained by restricting the form of constraints and/or rules.
Fusions of Description Logics and Abstract Description Systems
Authors: Baader F. , Lutz C. , Sturm H. , Wolter F.
Published: July 2018

Fusions are a simple way of combining logics. For normal modal logics, fusions have been investigated in detail. In particular, it is known that, under certain conditions, decidability transfers from the component logics to their fusion. Though description logics are closely related to modal logics, they are not necessarily normal. In addition, ABox reasoning in description logics is not covered by the results from modal logics. In this paper, we extend the decidability transfer results from normal modal logics to a large class of description logics. To cover different description logics in a uniform way, we introduce abstract description systems, which can be seen as a common generalization of description and modal logics, and show the transfer results in this general setting.
Improving the Efficiency of Inductive Logic Programming Through the
Use of Query Packs
Authors: Blockeel H. , Dehaspe L. , Demoen B. , Janssens G. , Ramon J. , Vandecasteele H.
Published: July 2018

Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described for executing such query packs. A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism. This claim is supported by empirical results obtained by incorporating support for query pack execution in two existing learning systems.
SMOTE: Synthetic Minority Over-sampling Technique
Authors: Chawla N. V. , Bowyer K. W. , Hall L. O. , Kegelmeyer W. P.
Published: July 2018

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Automatically Training a Problematic Dialogue Predictor for a Spoken Dialogue System
Authors: Walker M. A. , Langkilde-Geary I. , Wright Hastie H. , Wright J. , Gorin A.
Published: July 2018

Spoken dialogue systems promise efficient and natural access to a large variety of information sources and services from any phone. However, current spoken dialogue systems are deficient in their strategies for preventing, identifying and repairing problems that arise in the conversation. This paper reports results on automatically training a Problematic Dialogue Predictor to predict problematic human-computer dialogues using a corpus of 4692 dialogues collected with the 'How May I Help You' (SM) spoken dialogue system. The Problematic Dialogue Predictor can be immediately applied to the system's decision of whether to transfer the call to a human customer care agent, or be used as a cue to the system's dialogue manager to modify its behavior to repair problems, and even perhaps, to prevent them. We show that a Problematic Dialogue Predictor using automatically-obtainable features from the first two exchanges in the dialogue can predict problematic dialogues 13.2% more accurately than the baseline.
A Logic for Reasoning about Upper Probabilities
Authors: Halpern J. Y. , Pucella R.
Published: July 2018

We present a propositional logic to reason about the uncertainty of events, where the uncertainty is modeled by a set of probability measures assigning an interval of probability to each event. We give a sound and complete axiomatization for the logic, and show that the satisfiability problem is NP-complete, no harder than satisfiability for propositional logic.
Collective Intelligence, Data Routing and Braess' Paradox
Authors: Wolpert D. H. , Tumer K.
Published: July 2018

We consider the problem of designing the the utility functions of the utility-maximizing agents in a multi-agent system so that they work synergistically to maximize a global utility. The particular problem domain we explore is the control of network routing by placing agents on all the routers in the network. Conventional approaches to this task have the agents all use the Ideal Shortest Path routing Algorithm (ISPA). We demonstrate that in many cases, due to the side-effects of one agent's actions on another agent's performance, having agents use ISPA's is suboptimal as far as global aggregate cost is concerned, even when they are only used to route infinitesimally small amounts of traffic. The utility functions of the individual agents are not ``aligned'' with the global utility, intuitively speaking. As a particular example of this we present an instance of Braess' paradox in which adding new links to a network whose agents all use the ISPA results in a decrease in overall throughput. We also demonstrate that load-balancing, in which the agents' decisions are collectively made to optimize the global cost incurred by all traffic currently being routed, is suboptimal as far as global cost averaged across time is concerned. This is also due to `side-effects', in this case of current routing decision on future traffic. The mathematics of Collective Intelligence (COIN) is concerned precisely with the issue of avoiding such deleterious side-effects in multi-agent systems, both over time and space. We present key concepts from that mathematics and use them to derive an algorithm whose ideal version should have better performance than that of having all agents use the ISPA, even in the infinitesimal limit. We present experiments verifying this, and also showing that a machine-learning-based version of this COIN algorithm in which costs are only imprecisely estimated via empirical means (a version potentially applicable in the real world) also outperforms the ISPA, despite having access to less information than does the ISPA. In particular, this COIN algorithm almost always avoids Braess' paradox.
Imagining Alternative and Better Worlds: Isabel Fletcher Talks with Adele E. Clarke
Authors: Fletcher Isabel , Clarke Adele E.
Published: July 2018

In this interview, Adele Clarke and Isabel Fletcher discuss the different routes that led Clarke to science and technology studies (STS), the field’s increasing engagement with biomedical topics, and her perspectives on its character today. Clarke describes how women’s health activism and teaching feminist critiques of bioscience/biomedicine led her to participate in academic networks now known as feminist STS and trans-national reproduction studies. She reflects on the importance of inter-/trans-disciplinary collaboration in her work, but also raises concerns that the rapid expansion of the field has resulted in inadequate training for newcomers in the “theory-method packages” of STS, and hence poor quality scholarship. For her, the future of STS lies in approaches analyzing the complex intersections between technoscience, gender, race, (post)coloniality, and indigenous knowledges, and in its expansion beyond Europe and North America, to Asia, Central and South America, and Africa. In her following reflection, Isabel Fletcher considers the importance of inter/trans-disciplinarity for STS and highlights the role a politically engaged STS can play in imagining alternative and better worlds.
STS as a Program of Ontological Disobedience: Koichi Mikami Talks with Steve Woolgar
Authors: Mikami Koichi , Woolgar Steve
Published: July 2018

Is science and technology studies (STS) a luxury that our society cannot afford anymore? In this interview, Koichi Mikami tries to learn lessons from Steve Woolgar’s distinguished career on how the kind of sensibilities treasured within the field of STS and the type of critical engagement that its researchers aspire to might be best exercised in a changing landscape of higher education and academic research. Woolgar explains how he, at some key moments in his career, managed to create “a room” for reflexive thought and critical engagement in domains that could otherwise have been dominated by simple deterministic discourses. He explains that the questions of how and to whom you sell your criticism deserve as much attention as what your criticism is. A reflection by Koichi Mikami follows the interview.
It’s Important to Go to the Laboratory: Malte Ziewitz Talks with Michael Lynch
Authors: Ziewitz Malte , Lynch Michael
Published: July 2018

Why would anyone still want to go to the laboratory in 2018? In this interview, Michael Lynch answers this and other questions, reflecting on his own journey in, through, and alongside the field of science and technology studies (STS). Starting from his days as a student of Harold Garfinkel’s at UCLA to more recent times as editor of Social Studies of Science, Lynch talks about the rise of origin stories in the field; the role of ethnomethodology in his thinking; the early days of laboratory studies; why “turns” and “waves” might better be called “spins”; what he learned from David Edge; why we should be skeptical of the presumption that STS enhances the democratization of science; and why it might be time to “blow up STS”––an appealing idea that Malte Ziewitz takes up in his reflection following the interview.
“Things Can Be Done Here That Cannot So Easily Be Done Elsewhere”: Jane Calvert Talks with Arie Rip
Authors: Calvert Jane , Rip Arie
Published: July 2018

In this interview, Arie Rip talks to Jane Calvert about his life in STS and the history and future of the field. He begins in the late 1960s, when he started teaching a course in “chemistry and society.” He then gives a first-hand account of the formation of the European Association for the Study of Science and Technology (EASST) and the Society for the Social Studies of Science (4S), and the growth of research in the “melting pot” that STS became. He goes on to discuss recent work on new and emerging technologies and responsible research and innovation. His narrative shows the connections, historically and in the present, between the different strands of STS, including sociological work, science policy and innovation studies. He argues that we are seeing a “mainstreaming” of STS as it permeates other academic disciplines and arenas such as government agencies and charities. He suggests that this mainstreaming may mean STS will disappear as an independent discipline. In her reflection following the interview, Jane Calvert focuses on Rip’s point that STS is both a social movement and an academic discipline. She argues that this raises questions about what type of work we want to do as STS researchers, and how much freedom we have to choose.
The Whole World is Becoming Science Studies: Fadhila Mazanderani Talks with Bruno Latour
Authors: Mazanderani Fadhila , Latour Bruno
Published: July 2018

How to survive in this forest? How to keep it alive? Latour poses these questions in relation to the current global ecological crisis; but they are equally apt when applied to the “forest”––or to use Latour’s own metaphor, “biodiversity”––of STS. In his interview, Latour puts forward a particular vision for STS’s survival; a vision of STS as neither critical of nor ancillary to science, but where a tacit STS sensibility becomes integrated into science through education and collaboration. While Latour acknowledges the many differences of attitude and approach within STS, he also glosses over them, foregrounding as the defining feature of all those “infected” with STS the shared commitment to transforming science with the big “S” into something that can be studied empirically. The picture he paints is, unsurprisingly, a constructivist one, in which social and natural scientists, engineers, artists and politicians, “build worlds” together.  A reflection by Fadhila Mazanderani follows the interview.
Hepatitis A MGB TaqMan v1
Authors: M Mackay Ian , Finger Mitchell , Lyon Michael , A Northill Judy
Published: July 2018

This protocol was developed at this laboratory but had not been previously published. The protocol aims explicitly to amplify hepatitis A virus (HAV) strains and not other virus species. The assay targets the 5' untranslated region and is designed as a qualitative test for investigating suspected human cases of HAV infection.
Production of lentiviral vectors pseudotyped with influenza neuraminidase (NA) v1
Authors: Carnell George , Biuso Fabrizio , Montomoli Emanuele , Temperton Nigel
Published: July 2018

Lentiviral vectors pseudotyped with influenza surface glycoproteins represent an alternative to wild type virus for serological assays. The influenza neuraminidase (NA) has the ability to bud and release new virions with or without the contribution of Haemagglutinin (HA). Influenza pseudotypes expressing NA alone, or with HA, can be produced to evaluate the antibody response against NA using an enzyme-linked lectin assay (ELLA). The expression of an avian HA with human NAs has enabled the detection of specific antibody reponses against the human circulating subtypes of NA. This protocol describes the efficient production of NA pseudotypes for these ELLA assays. The pseudotypes have H11 HA on the surface as this will not react with human sera. Figure 1. Cartoon schematic of lentiviral vectors pseudotyped with NA or HA+NA for use in ELLA assays.
La alta nobleza y el poder en el estado durante el antiguo régimen (siglos XVI-XVIII): un balance cuantitativo
Authors: Lorenzo Cadarso Pedro Luis
Published: July 2018

Este artículo estudia la evolución de la presencia de la nobleza titulada en los distintos ámbitos de poder estatal durante los siglos XVI al XVIII, comparándola con la de los otros colectivos sociales que aspiraban también a ocupar cargos relevantes. Se hace desde una perspectiva estrictamente cuantitativa, al objeto de permitir evaluar la magnitud de los fenómenos que se describen y elaborar una cronología precisa de los mismos.
Justicia y orden social: delincuencia y represión del delito en Logroño en el siglo XVIII
Authors: Gómez Urdañez José Luis , Ibáñez Castro Juan , Ilzarbe López Isabel , Moreno Galilea Diego
Published: July 2018

A través de los expedientes originados en el marco de la justicia impartida por el corregidor de Logroño en el siglo XVIII intentamos comprender el amplio campo de la delincuencia y el papel moralizador del juez. Los procesos judiciales, desde la denuncia a la sentencia, pasando generalmente por la estancia preventiva en la cárcel, contienen información sobre el orden social, que tras haber sido quebrantado, ha de ser de nuevo, institucionalmente, reparado, en beneficio de Dios y el rey.
Los orígenes de la prensa liberal: el caso del "Periódico político y mercantil de la Villa de Reus" (1813-1814)
Authors: Fàbregas Roig Josep
Published: July 2018

El decreto de noviembre de 1810, de libertad de imprenta, aprobado por las Cortes de Cádiz, facilitó la aparición de un conjunto de periódicos de ideología progresista. El 4 de octubre de 1813 se publicaba en Reus el Periódico político y mercantil de la Villa de Reus, un referente de la prensa liberal de inicios del siglo XIX. Sus noticias se referían al proceso constitucional español, al desarrollo de la guerra contra los franceses y a informaciones de tipo comercial. Su editor fue el prestigioso médico Jaume Ardévol
Levinas y la alteridad: cinco planos
Authors: Fernández Guerrero Olaya
Published: July 2018

Este trabajo revisa las concepciones de la alteridad desarrolladas por Levinas, y muestra cinco planos fundamentales en torno a los que este filósofo plantea esta noción. Se trata de las dimensiones metafísica, religiosa, subjetiva, lingüística y ética, todas ellas estrechamente conectadas entre si y que ofrecen múltiples perspectivas para reflexionar sobre la alteridad.