Semantics for possibilistic answer set programs: uncertain rules versus rules with uncertain conclusions

Although Answer Set Programming (ASP) is a powerful framework for declarative problem solving, it cannot in an intuitive way handle situations in which some rules are uncertain, or in which it is more important to satisfy some constraints than others. Possibilistic ASP (PASP) is a natural extension of ASP in which certainty weights are associated with each rule. In this paper we contrast two different views on interpreting the weights attached to rules. Under the first view, weights reflect the certainty with which we can conclude the head of a rule when its body is satisfied. Under the second view, weights reflect the certainty that a given rule restricts the considered epistemic states of an agent in a valid way, i.e. it is the certainty that the rule itself is correct. The first view gives rise to a set of weighted answer sets, whereas the second view gives rise to a weighted set of classical answer sets.

Characterizing and Extending Answer Set Semantics using Possibility Theory

Answer Set Programming (ASP) is a popular framework for modelling combinatorial problems. However, ASP cannot be used easily for reasoning about uncertain information. Possibilistic ASP (PASP) is an extension of ASP that combines possibilistic logic and ASP. In PASP a weight is associated with each rule, whereas this weight is interpreted as the certainty with which the conclusion can be established when the body is known to hold. As such, it allows us to model and reason about uncertain information in an intuitive way. In this paper we present new semantics for PASP in which rules are interpreted as constraints on possibility distributions. Special models of these constraints are then identified as possibilistic answer sets. In addition, since ASP is a special case of PASP in which all the rules are entirely certain, we obtain a new characterization of ASP in terms of constraints on possibility distributions. This allows us to uncover a new form of disjunction, called weak disjunction, that has not been previously considered in the literature. In addition to introducing and motivating the semantics of weak disjunction, we also pinpoint its computational complexity. In particular, while the complexity of most reasoning tasks coincides with standard disjunctive ASP, we find that brave reasoning for programs with weak disjunctions is easier.

Using Answer Set Programming for Solving Boolean Games

Boolean games are a framework for reasoning about the rational behaviour of agents, whose goals are formalized using propositional formulas. They offer an attractive alternative to normal-form games, because they allow for a more intuitive and more compact encoding. Unfortunately, however, there is currently no general, tailor-made method available to compute the equilibria of Boolean games. In this paper, we introduce a method for finding the pure Nash equilibria based on disjunctive answer set programming. Our method is furthermore capable of finding the core elements and the Pareto optimal equilibria, and can easily be modified to support other forms of optimality, thanks to the declarative nature of disjunctive answer set programming. Experimental results clearly demonstrate the effectiveness of the proposed method.

Possibilistic Answer Set Programming Revisited

Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical ASP programs in terms of possibilistic logic where an ASP program specifies a set of constraints on possibility distributions. This characterization is then naturally generalized to define answer sets of PASP programs. We furthermore provide a syntactic counterpart, leading to a possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we show how our framework can readily be implemented using standard ASP solvers.

Communicating Answer Set Programs

Answer set programming is a form of declarative programming that has proven very successful in succinctly formulating and solving complex problems. Although mechanisms for representing and reasoning with the combined answer set programs of multiple agents have already been proposed, the actual gain in expressivity when adding communication has not been thoroughly studied. We show that allowing simple programs to talk to each other results in the same expressivity as adding negation-as-failure. Furthermore, we show that the ability to focus on one program in a network of simple programs results in the same expressivity as adding disjunction in the head of the rules.

Hidden Markov Models With Set-Valued Parameters

Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filtering and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.

Retrieving Similar Discussion Forum Threads: A Structure based Approach

Online forums are becoming a popular way of finding useful
information on the web. Search over forums for existing discussion
threads so far is limited to keyword-based search due
to the minimal effort required on part of the users. However,
it is often not possible to capture all the relevant context in a
complex query using a small number of keywords. Examplebased
search that retrieves similar discussion threads given
one exemplary thread is an alternate approach that can help
the user provide richer context and vastly improve forum
search results. In this paper, we address the problem of
finding similar threads to a given thread. Towards this, we
propose a novel methodology to estimate similarity between
discussion threads. Our method exploits the thread structure
to decompose threads in to set of weighted overlapping
components. It then estimates pairwise thread similarities
by quantifying how well the information in the threads are
mutually contained within each other using lexical similarities
between their underlying components. We compare our
proposed methods on real datasets against state-of-the-art
thread retrieval mechanisms wherein we illustrate that our
techniques outperform others by large margins on popular
retrieval evaluation measures such as NDCG, MAP, Precision@k
and MRR. In particular, consistent improvements of
up to 10% are observed on all evaluation measures

Answer set programs with optional rules: a possibilistic approach

Many problems in artificial intelligence can be encoded as answer set programs (ASP) in which some rules are uncertain. ASP programs with incorrect rules may have erroneous conclusions, but due to the non-monotonic nature of ASP, omitting a correct rule may also lead to errors. To derive the most certain conclusions from an uncertain ASP program, we thus need to consider all situations in which some, none, or all of the least certain rules are omitted. This corresponds to treating some rules as optional and reasoning about which conclusions remain valid regardless of the inclusion of these optional rules. While a version of possibilistic ASP (PASP) based on this view has recently been introduced, no implementation is currently available. In this paper we propose a simulation of the main reasoning tasks in PASP using (disjunctive) ASP programs, allowing us to take advantage of state-of-the-art ASP solvers. Furthermore, we identify how several interesting AI problems can be naturally seen as special cases of the considered reasoning tasks, including cautious abductive reasoning and conformant planning. As such, the proposed simulation enables us to solve instances of the latter problem types that are more general than what current solvers can handle.

A primer for use of genetic tools in selecting and testing the suitability of set-aside sites protected from deep-sea seafloor massive sulfide mining activities

Seafloor massive sulfide (SMS) mining will likely occur at hydrothermal systems in the near future. Alongside their mineral wealth, SMS deposits also have considerable biological value. Active SMS deposits host endemic hydrothermal vent communities, whilst inactive deposits support communities of deep water corals and other suspension feeders. Mining activities are expected to remove all large organisms and suitable habitat in the immediate area, making vent endemic organisms particularly at risk from habitat loss and localised extinction. As part of environmental management strategies designed to mitigate the effects of mining, areas of seabed need to be protected to preserve biodiversity that is lost at the mine site and to preserve communities that support connectivity among populations of vent animals in the surrounding region. These "set-aside" areas need to be biologically similar to the mine site and be suitably connected, mostly by transport of larvae, to neighbouring sites to ensure exchange of genetic material among remaining populations. Establishing suitable set-asides can be a formidable task for environmental managers, however the application of genetic approaches can aid set-aside identification, suitability assessment and monitoring. There are many genetic tools available, including analysis of mitochondrial DNA (mtDNA) sequences (e.g. COI or other suitable mtDNA genes) and appropriate nuclear DNA markers (e.g. microsatellites, single nucleotide polymorphisms), environmental DNA (eDNA) techniques and microbial metagenomics. When used in concert with traditional biological survey techniques, these tools can help to identify species, assess the genetic connectivity among populations and assess the diversity of communities. How these techniques can be applied to set-aside decision making is discussed and recommendations are made for the genetic characteristics of set-aside sites. A checklist for environmental regulators forms a guide to aid decision making on the suitability of set-aside design and assessment using genetic tools. This non-technical primer document represents the views of participants in the VentBase 2014 workshop.

Towards a spectroscopically accurate set of potentials for heavy hydride laser cooling candidates: effective core potential calculations of BaH

BaH (and its isotopomers) is an attractive molecular candidate for laser cooling to ultracold temperatures and a potential precursor for the production of ultracold gases of hydrogen and deuterium. The theoretical challenge is to simulate the laser cooling cycle as reliably as possible and this paper addresses the generation of a highly accurate ab initio $^{2}\Sigma^+$ potential for such studies. The performance of various basis sets within the multi-reference configuration-interaction (MRCI) approximation with the Davidson correction (MRCI+Q)is tested and taken to the Complete Basis Set (CBS) limit. It is shown that the calculated molecular constants using a 46 electron Effective Core-Potential (ECP) and even-tempered augmented polarized core-valence basis sets (aug-pCV$n$Z-PP, n= 4 and 5) but only including three active electrons in the MRCI calculation are in excellent agreement with the available experimental values. The predicted dissociation energy De for the X$^2\Sigma^+$ state (extrapolated to the CBS limit) is 16895.12 cm$^{-1}$ (2.094 eV), which agrees within 0.1$\%$ of a revised experimental value of <16910.6 cm$^{-1}$, while the calculated re is within 0.03 pm of the experimental result.

Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies

Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost.

Detecting obfuscated malware using reduced opcode set and optimised runtime trace

The research presented, investigates the optimal set of operational codes (opcodes) that create a robust indicator of malicious software (malware) and also determines a program’s execution duration for accurate classification of benign and malicious software. The features extracted from the dataset are opcode density histograms, extracted during the program execution. The classifier used is a support vector machine and is configured to select those features to produce the optimal classification of malware over different program run lengths. The findings demonstrate that malware can be detected using dynamic analysis with relatively few opcodes.

Variable Window Power Spectral Density Attack

Side channel attacks permit the recovery of the secret key held within a cryptographic device. This paper presents a new EM attack in the frequency domain, using a power spectral density analysis that permits the use of variable spectral window widths for each trace of the data set and demonstrates how this attack can therefore overcome both inter-and intra-round random insertion type countermeasures. We also propose a novel re-alignment method exploiting the minimal power markers exhibited by electromagnetic emanations. The technique can be used for the extraction and re-alignment of round data in the time domain.