12,129 research outputs found
Handling Defeasibilities in Action Domains
Representing defeasibility is an important issue in common sense reasoning.
In reasoning about action and change, this issue becomes more difficult because
domain and action related defeasible information may conflict with general
inertia rules. Furthermore, different types of defeasible information may also
interfere with each other during the reasoning. In this paper, we develop a
prioritized logic programming approach to handle defeasibilities in reasoning
about action. In particular, we propose three action languages {\cal AT}^{0},
{\cal AT}^{1} and {\cal AT}^{2} which handle three types of defeasibilities in
action domains named defeasible constraints, defeasible observations and
actions with defeasible and abnormal effects respectively. Each language with a
higher superscript can be viewed as an extension of the language with a lower
superscript. These action languages inherit the simple syntax of {\cal A}
language but their semantics is developed in terms of transition systems where
transition functions are defined based on prioritized logic programs. By
illustrating various examples, we show that our approach eventually provides a
powerful mechanism to handle various defeasibilities in temporal prediction and
postdiction. We also investigate semantic properties of these three action
languages and characterize classes of action domains that present more
desirable solutions in reasoning about action within the underlying action
languages.Comment: 49 pages, 1 figure, to be appeared in journal Theory and Practice
Logic Programmin
Computing Preferred Answer Sets by Meta-Interpretation in Answer Set Programming
Most recently, Answer Set Programming (ASP) is attracting interest as a new
paradigm for problem solving. An important aspect which needs to be supported
is the handling of preferences between rules, for which several approaches have
been presented. In this paper, we consider the problem of implementing
preference handling approaches by means of meta-interpreters in Answer Set
Programming. In particular, we consider the preferred answer set approaches by
Brewka and Eiter, by Delgrande, Schaub and Tompits, and by Wang, Zhou and Lin.
We present suitable meta-interpreters for these semantics using DLV, which is
an efficient engine for ASP. Moreover, we also present a meta-interpreter for
the weakly preferred answer set approach by Brewka and Eiter, which uses the
weak constraint feature of DLV as a tool for expressing and solving an
underlying optimization problem. We also consider advanced meta-interpreters,
which make use of graph-based characterizations and often allow for more
efficient computations. Our approach shows the suitability of ASP in general
and of DLV in particular for fast prototyping. This can be fruitfully exploited
for experimenting with new languages and knowledge-representation formalisms.Comment: 34 pages, appeared as a Technical Report at KBS of the Vienna
University of Technology, see http://www.kr.tuwien.ac.at/research/reports
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
Prioritized Norms and Defaults in Formal Argumentation
International audienceDeontic logic sentences define what an agent ought to do when faced with a set of norms. These norms may come into conflict such that a priority ordering over them is necessary to resolve these conflicts. Dung’s seminal paper raises the still open challenge to use formal argumentation to represent non monotonic logics, highlight- ing its value to exchange, communicate and resolve possibly conflicting viewpoints in distributed scenarios. In this paper, we propose a formal framework to study various properties of prioritized non monotonic reasoning in formal argumentation, in line with this idea. More precisely, we show how a version of prioritized default logic and Brewka-Eiter’s construction in answer set programming can be obtained in argumentation via the weakest and last link principles. We also show how to represent Hansen’s recent construction for prioritized normative reasoning by adding arguments using weak contraposition via permissive norms, and their relationship to Caminada’s “hang yourself” arguments
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