Hybrid Probabilistic Logic Programs as Residuated Logic Programs

In this paper we show the embedding of Hybrid Probabilistic Logic Programs into the rather general framework of Residuated Logic Programs, where the main results of (definite) logic programming are validly extrapolated, namely the extension of the immediate consequences operator of van Emden and Kowalski. The importance of this result is that for the first time a framework encompassing several quite distinct logic programming semantics is described, namely Generalized Annotated Logic Programs, Fuzzy Logic Programming, Hybrid Probabilistic Logic Programs, and Possibilistic Logic Programming. Moreover, the embedding provides a more general semantical structure paving the way for defining paraconsistent probabilistic reasoning with a logic programming semantics.     

Joint production model: A note on a connection between market prices and CP anchor values

This paper focuses on a justification of the assumption ‘In a joint production competitive industry, market prices are inversely proportional to anchor values’. This assumption is important to underpin the property ‘In a competitive industry the maximum profit mix and the best technological mix coincide’.     

The impact of self‐confidence on the compromise effect

This research reports an investigation into whether the personality aspect of self‐confidence affects the compromise effect. We hypothesize that highly self‐confident people have greater certainty in making decisions and are more attracted to risk‐taking, which makes them less likely to choose the safe or middle option in a large choice set. The three studies involved are conducted using between‐ and within‐subjects experimental designs. Various product categories are used to generalize the findings. Study 1 looks at purchasing decisions and utilizes three scales of self‐confidence, risk preference, and uncertainty; it demonstrates that consumers with high self‐confidence are less likely to choose a compromise option due to high certainty in their decision‐making. Study 2 discovers that people with low self‐confidence are more likely to choose the middle option in a risky condition than in a nonrisky condition. Study 3 decomposes self‐confidence into general and specific self‐confidence, and reveals that people with low general self‐confidence and low specific self‐confidence are more likely to choose the middle option.     

The Refined Extension Principle for Semantics of Dynamic Logic Programming

Over recent years, various semantics have been proposed for dealing with updates in the setting of logic programs. The availability of different semantics naturally raises the question of which are most adequate to model updates. A systematic approach to face this question is to identify general principles against which such semantics could be evaluated. In this paper we motivate and introduce a new such principle the refined extension principle. Such principle is complied with by the stable model semantics for (single) logic programs. It turns out that none of the existing semantics for logic program updates, even though generalisations of the stable model semantics, comply with this principle. For this reason, we define a refinement of the dynamic stable model semantics for Dynamic Logic Programs that complies with the principle.     

Stepwise contouring of efficient criterion vectors for large-scale MOLP

In this paper a new method of presenting the overall (focused) structure of the efficient criterion vectors (N) for large-scale MOLP is proposed. The proposed algorithm ASEOV (approximation of the set of efficient objective vectors) determines the representative subset of N and ensures full coverage of N , with corresponding coverage precision indicated. ASEOV works in objective space directly and eliminates unnecessary computational effort at the collapsing extreme points of X , which are transformed to non-extreme points in objective space. The Tchebycheff metric is employed to measure the coverage precision. ASEOV allows a decision maker (DM) to control the determination procedure by assessing the coverage allowance for each criterion. When the DM's preference is available, ASEOV can focus contouring on the subset of N which fits with the extracted preference. Combined with proper interactive methods, this focused contour over N can reduce the DM's burden, inconsistency or cognitive bias in assessing his preference from which to derive the final best-compromise solution. An illustrative example is presented.     

Using a Compromise Programming Framework to Integrating Spatially Specific Preference Information for Forest Management Problems

The development of forest management plans is a multi‐criteria problem that usually involves multiple stakeholders. In forested recreational areas or natural parks, stakeholders could include government officials, forest companies and visitors to the area. With the progress of mobile phone technology, preference information (including aesthetic values) can be conveniently obtained from stakeholders while they are in the forest. This paper develops a methodological approach that incorporates place‐specific opinion preference into the development of alternative forest management plans. Through the use of a modified Compromise Programming approach, prospective alternatives are developed, which optimize the opinions of stakeholders and the quantity of timber over time depending on specified parameters. The analysis is based on a small simulated forest holding and place‐specific simulated opinion data. To allow for a comparison between potential stakeholder groups, the opinion data was generated by a method that produced three separate groups. The results indicate the practical use of place‐specific preferences and provide a means to incorporate the information to generate possible alternative plans. In addition, the analysis highlights that by segregating the preference information into groups, the planning of more specific courses of actions can be made. Copyright © 2015 John Wiley & Sons, Ltd.