10 research outputs found
On the Complexity of Finding Second-Best Abductive Explanations
While looking for abductive explanations of a given set of manifestations, an
ordering between possible solutions is often assumed. The complexity of
finding/verifying optimal solutions is already known. In this paper we consider
the computational complexity of finding second-best solutions. We consider
different orderings, and consider also different possible definitions of what a
second-best solution is
Compilability of Abduction
Abduction is one of the most important forms of reasoning; it has been
successfully applied to several practical problems such as diagnosis. In this
paper we investigate whether the computational complexity of abduction can be
reduced by an appropriate use of preprocessing. This is motivated by the fact
that part of the data of the problem (namely, the set of all possible
assumptions and the theory relating assumptions and manifestations) are often
known before the rest of the problem. In this paper, we show some complexity
results about abduction when compilation is allowed
Parameter Compilation
In resolving instances of a computational problem, if multiple instances of
interest share a feature in common, it may be fruitful to compile this feature
into a format that allows for more efficient resolution, even if the
compilation is relatively expensive. In this article, we introduce a formal
framework for classifying problems according to their compilability. The basic
object in our framework is that of a parameterized problem, which here is a
language along with a parameterization---a map which provides, for each
instance, a so-called parameter on which compilation may be performed. Our
framework is positioned within the paradigm of parameterized complexity, and
our notions are relatable to established concepts in the theory of
parameterized complexity. Indeed, we view our framework as playing a unifying
role, integrating together parameterized complexity and compilability theory
Proceedings of the 11th Workshop on Nonmonotonic Reasoning
These are the proceedings of the 11th Nonmonotonic Reasoning Workshop. The aim of this series is to bring together active researchers in the broad area of nonmonotonic reasoning, including belief revision, reasoning about actions, planning, logic programming, argumentation, causality, probabilistic and possibilistic approaches to KR, and other related topics. As part of the program of the 11th workshop, we have assessed the status of the field and discussed issues such as: Significant recent achievements in the theory and automation of NMR; Critical short and long term goals for NMR; Emerging new research directions in NMR; Practical applications of NMR; Significance of NMR to knowledge representation and AI in general
Proceedings of the Workshop on Change of Representation and Problem Reformulation
The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning