Anbu Yue

Handling Inconsistency In Distributed Software Requirements Specifications Based On Prioritized Merging

Developing a desirable framework for handling inconsistencies in software requirements specifications is a challenging problem. It has been widely recognized that the relative priority of requirements can help developers to make some necessary trade-off decisions for resolving con- flicts. However, for most distributed development such as viewpoints-based approaches, different stakeholders may assign different levels of priority to the same shared requirements statement from their own perspectives. The disagreement in the local levels of priority assigned to the same shared requirements statement often puts developers into a dilemma during the inconsistency handling process. The main contribution of this paper is to present a prioritized merging-based framework for handling inconsistency in distributed software requirements specifications. Given a set of distributed inconsistent requirements collections with the local prioritization, we first construct a requirements specification with a prioritization from an overall perspective. We provide two approaches to constructing a requirements specification with the global prioritization, including a merging-based construction and a priority vector-based construction. Following this, we derive proposals for handling inconsistencies from the globally prioritized requirements specification in terms of prioritized merging. Moreover, from the overall perspective, these proposals may be viewed as the most appropriate to modifying the given inconsistent requirements specification in the sense of the ordering relation over all the consistent subsets of the requirements specification. Finally, we consider applying negotiation-based techniques to viewpoints so as to identify an acceptable common proposal from these proposals.

Measuring the Ignorance and Degree of Satisfaction for Answering Queries in Imprecise Probabilistic Logic Programs

In probabilistic logic programming, given a query, either a probability interval or a precise probability obtained by using the maximum entropy principle is returned for the query. The former can be noninformative (e.g., interval [0,1]) and the reliability of the latter is questionable when the priori knowledge is imprecise. To address this problem, in this paper, we propose some methods to quantitatively measure if a probability interval or a single probability is sufficient for answering a query. We first propose an approach to measuring the ignorance of a probabilistic logic program with respect to a query. The measure of ignorance (w.r.t. a query) reflects how reliable a precise probability for the query can be and a high value of ignorance suggests that a single probability is not suitable for the query. We then propose a method to measure the probability that the exact probability of a query falls in a given interval, e.g., a second order probability. We call it the degree of satisfaction. If the degree of satisfaction is high enough w.r.t. the query, then the given interval can be accepted as the answer to the query. We also provide properties of the two measures and use an example to demonstrate the significance of the measures.

Approaches to Constructing a Stratified Merged Knowledge Base

Many merging operators have been proposed to merge either flat or stratified knowledge bases. The result of merging by such an operator is a flat base (or a set of models of the merged base) irrespective of whether the original ones are flat or stratified. The drawback of obtaining a flat merged base is that information about more preferred knowledge (formulae) versus less preferred knowledge is not explicitly represented, and this information can be very useful when deciding which formulae should be retained when there is a conflict. Therefore, it can be more desirable to return a stratified knowledge base as a merged result. A straightforward approach is to deploy the preference relation over possible worlds obtained after merging to reconstruct such a base. However, our study shows that such an approach can produce a poor result, that is, preference relations over possible worlds obtained after merging are not suitable for reconstructing a merged stratified base. Inspired by the Condorcet method in voting systems, we propose an alternative method to stratify a set of possible worlds given a set of stratified bases and take the stratification of possible worlds as the result of merging. Based on this, we provide a family of syntax-based methods and a family of model-based methods to construct a stratified merged knowledge base. In the syntax based methods, the formulae contained in the merged knowledge base are from the original individual knowledge bases. In contrast, in the model based methods, some additional formulae may be introduced into the merged knowledge base and no information in the original knowledge bases is lost. Since the merged result is a stratified knowledge base, the commonly agreed knowledge together with a preference relation over this knowledge can be extracted from the original knowledge bases.

Adaptive dialogue strategy selection through imprecise probabilistic query answering

In a human-computer dialogue system, the dialogue strategy can range from very restrictive to highly flexible. Each specific dialogue style has its pros and cons and a dialogue system needs to select the most appropriate style for a given user. During the course of interaction, the dialogue style can change based on a users response and the system observation of the user. This allows a dialogue system to understand a user better and provide a more suitable way of communication. Since measures of the quality of the users interaction with the system can be incomplete and uncertain, frameworks for reasoning with uncertain and incomplete information can help the system make better decisions when it chooses a dialogue strategy. In this paper, we investigate how to select a dialogue strategy based on aggregating the factors detected during the interaction with the user. For this purpose, we use probabilistic logic programming (PLP) to model probabilistic knowledge about how these factors will affect the degree of freedom of a dialogue. When a dialogue system needs to know which strategy is more suitable, an appropriate query can be executed against the PLP and a probabilistic solution with a degree of satisfaction is returned. The degree of satisfaction reveals how much the system can trust the probability attached to the solution.

Using probabilistic logic for dialogue strategy selection

Automated dialogue strategies range from very restrictive (limiting a person to yes/no responses) to highly flexible (the person can say what they like): dialogue strategies offer different ‘degrees of freedom’. Strategies may change midtransaction as a system responds to a user’s verbal and non-verbal input. Our prototype system aggregates a selection of strategy-influencing factors and uses a probabilistic logic program (PLP) to exploit developers’ understanding of how typical factors affect the degree of freedom in a dialogue. Not only is it possible to compute a ‘crisp’ probability that a ‘free’ dialogue strategy is appropriate in particular circumstances, but it is also possible to calculate degrees of satisfaction indicating how reliable probabilities within particular ranges are as the answer to a PLP query.

A ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge

Ligand prediction has been driven by a fundamental desire to understand more about how biomolecules recognize their ligands and by the commercial imperative to develop new drugs. Most of the current available software systems are very complex and time-consuming to use. Therefore, developing simple and efficient tools to perform initial screening of interesting compounds is an appealing idea. In this paper, we introduce our tool for very rapid screening for likely ligands (either substrates or inhibitors) based on reasoning with imprecise probabilistic knowledge elicited from past experiments. Probabilistic knowledge is input to the system via a user-friendly interface showing a base compound structure. A prediction of whether a particular compound is a substrate is queried against the acquired probabilistic knowledge base and a probability is returned as an indication of the prediction. This tool will be particularly useful in situations where a number of similar compounds have been screened experimentally, but information is not available for all possible members of that group of compounds. We use two case studies to demonstrate how to use the tool.