34 research outputs found
Answer Set Programming and Combinatorial Voting
We show how Logic Programming with Ordered
Disjunction (LPOD), the extension of answer
set programming for handling preferences, may
be used for representing and solving collective
decision making problems. We present the
notion of combinatorial vote problem in the
context of LPOD and define various types of
vote rules, used as decision criteria for
determining optimal candidate for a group of voters.
15 min presentatio
Reasoning by Cases in Structured Argumentation
We extend the framework for structured argumentation so as to allow
applications of the reasoning by cases inference scheme for defeasible
arguments. Given an argument with conclusion ` or ', an argument based on
with conclusion , and an argument based on with conclusion , we
allow the construction of an argument with conclusion . We show how our
framework leads to different results than other approaches in non-monotonic
logic for dealing with disjunctive information, such as disjunctive default
theory or approaches based on the OR-rule (which allows to derive a defeasible
rule `If ( or ) then ', given two defeasible rules `If then '
and `If then '). We raise new questions regarding the subtleties of
reasoning defeasibly with disjunctive information, and show that its
formalization is more intricate than one would presume.Comment: Proceedings of SAC/KRR 201
Borhan: A Novel System for Prioritized Default Logic
Prioritized Default Logic presents an optimal solution for addressing
real-world problems characterized by incomplete information and the need to
establish preferences among diverse scenarios. Although it has reached great
success in the theoretical aspect, its practical implementation has received
less attention. In this article, we introduce Borhan, a system designed and
created for prioritized default logic reasoning. To create an effective system,
we have refined existing default logic definitions, including the extension
concept, and introduced novel concepts. In addition to its theoretical merits,
Borhan proves its practical utility by efficiently addressing a range of
prioritized default logic problems. In addition, one of the advantages of our
system is its ability to both store and report the explanation path for any
inferred triple, enhancing transparency and interpretability. Borhan is offered
as an open-source system, implemented in Python, and even offers a simplified
Java version as a plugin for the Protege ontology editor. Borhan thus
represents a significant step forward in bridging the gap between the
theoretical foundations of default logic and its real-world applications
Accepting the natural order of rules in a logic program with preferences
Preference is a natural part of common sense reasoning. It allows us to select preferred conclusions from broader range of alternative conclusions. It is typically specified on parts of conclusions or on rules. Different semantics have been proposed that deal with preference on rules. None fully meets our requirements.
We are interested in a descriptive approach to preference handling in logic programs under answer set semantics that always selects preferred answer set when standard one exists. Existing semantics that meet this criterion also give non intuitive conclusions on some programs. We think this kind of problem is related to the problem of not accepting natural order of rules induced by underlying answer set semantics.
Our goal is to define semantics that would always select preferred answer set when standard one exists, accept natural order on rules, and satisfy principles for preference handling
Translating P-log, LPMLN, LPOD, and CR-Prolog2 into Standard Answer Set Programs
Answer set programming (ASP) is a particularly useful approach for nonmonotonic reasoning in knowledge representation. In order to handle quantitative and qualitative reasoning, a number of different extensions of ASP have been invented, such as quantitative extensions LP^{MLN} and P-log, and qualitative extensions LPOD, and CR-Prolog_2.
Although each of these formalisms introduced some new and unique concepts, we present reductions of each of these languages into the standard ASP language, which not only gives us an alternative insight into the semantics of these extensions in terms of the standard ASP language, but also shows that the standard ASP is capable of representing quantitative uncertainty and qualitative uncertainty. What\u27s more, our translations yield a way to tune the semantics of LPOD and CR-Prolog_2. Since the semantics of each formalism is represented in ASP rules, we can modify their semantics by modifying the corresponding ASP rules.
For future work, we plan to create a new formalism that is capable of representing quantitative and qualitative uncertainty at the same time. Since LPOD rules are simple and informative, we will first try to include quantitative preference into LPOD by adding the concept of weight and tune the semantics of LPOD by modifying the translated standard ASP rules
04271 Abstracts Collection -- Preferences: Specification, Inference, Applications
From 27.06.04 to 02.07.04, the Dagstuhl Seminar 04271 ``Preferences: Specification, Inference, Applications\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Implementing Preferences with asprin
asprin offers a framework for expressing and evaluating combinations of quantitative and qualitative preferences among the stable models of a logic program. In this paper, we demonstrate the generality and flexibility of the methodology by showing how easily existing preference relations can be implemented in asprin. Moreover, we show how the computation of optimal stable models can be improved by using declarative heuristics. We empirically evaluate our contributions and contrast them with dedicated implementations. Finally, we detail key aspects of asprin’s implementation.Full Tex