20,637 research outputs found
Redundancy in Logic III: Non-Mononotonic Reasoning
Results about the redundancy of circumscriptive and default theories are
presented. In particular, the complexity of establishing whether a given theory
is redundant is establihsed.Comment: minor correction
A reconstruction of the multipreference closure
The paper describes a preferential approach for dealing with exceptions in
KLM preferential logics, based on the rational closure. It is well known that
the rational closure does not allow an independent handling of the inheritance
of different defeasible properties of concepts. Several solutions have been
proposed to face this problem and the lexicographic closure is the most notable
one. In this work, we consider an alternative closure construction, called the
Multi Preference closure (MP-closure), that has been first considered for
reasoning with exceptions in DLs. Here, we reconstruct the notion of MP-closure
in the propositional case and we show that it is a natural variant of Lehmann's
lexicographic closure. Abandoning Maximal Entropy (an alternative route already
considered but not explored by Lehmann) leads to a construction which exploits
a different lexicographic ordering w.r.t. the lexicographic closure, and
determines a preferential consequence relation rather than a rational
consequence relation. We show that, building on the MP-closure semantics,
rationality can be recovered, at least from the semantic point of view,
resulting in a rational consequence relation which is stronger than the
rational closure, but incomparable with the lexicographic closure. We also show
that the MP-closure is stronger than the Relevant Closure.Comment: 57 page
KR: An Architecture for Knowledge Representation and Reasoning in Robotics
This paper describes an architecture that combines the complementary
strengths of declarative programming and probabilistic graphical models to
enable robots to represent, reason with, and learn from, qualitative and
quantitative descriptions of uncertainty and knowledge. An action language is
used for the low-level (LL) and high-level (HL) system descriptions in the
architecture, and the definition of recorded histories in the HL is expanded to
allow prioritized defaults. For any given goal, tentative plans created in the
HL using default knowledge and commonsense reasoning are implemented in the LL
using probabilistic algorithms, with the corresponding observations used to
update the HL history. Tight coupling between the two levels enables automatic
selection of relevant variables and generation of suitable action policies in
the LL for each HL action, and supports reasoning with violation of defaults,
noisy observations and unreliable actions in large and complex domains. The
architecture is evaluated in simulation and on physical robots transporting
objects in indoor domains; the benefit on robots is a reduction in task
execution time of 39% compared with a purely probabilistic, but still
hierarchical, approach.Comment: The paper appears in the Proceedings of the 15th International
Workshop on Non-Monotonic Reasoning (NMR 2014
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