32,917 research outputs found
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
Quantum-mechanical machinery for rational decision-making in classical guessing game
In quantum game theory, one of the most intriguing and important questions
is, "Is it possible to get quantum advantages without any modification of the
classical game?" The answer to this question so far has largely been negative.
So far, it has usually been thought that a change of the classical game setting
appears to be unavoidable for getting the quantum advantages. However, we give
an affirmative answer here, focusing on the decision-making process (we call
'reasoning') to generate the best strategy, which may occur internally, e.g.,
in the player's brain. To show this, we consider a classical guessing game. We
then define a one-player reasoning problem in the context of the
decision-making theory, where the machinery processes are designed to simulate
classical and quantum reasoning. In such settings, we present a scenario where
a rational player is able to make better use of his/her weak preferences due to
quantum reasoning, without any altering or resetting of the classically defined
game. We also argue in further analysis that the quantum reasoning may make the
player fail, and even make the situation worse, due to any inappropriate
preferences.Comment: 9 pages, 10 figures, The scenario is more improve
An encompassing framework for Paraconsistent Logic Programs
AbstractWe propose a framework which extends Antitonic Logic Programs [Damásio and Pereira, in: Proc. 6th Int. Conf. on Logic Programming and Nonmonotonic Reasoning, Springer, 2001, p. 748] to an arbitrary complete bilattice of truth-values, where belief and doubt are explicitly represented. Inspired by Ginsberg and Fitting's bilattice approaches, this framework allows a precise definition of important operators found in logic programming, such as explicit and default negation. In particular, it leads to a natural semantical integration of explicit and default negation through the Coherence Principle [Pereira and Alferes, in: European Conference on Artificial Intelligence, 1992, p. 102], according to which explicit negation entails default negation. We then define Coherent Answer Sets, and the Paraconsistent Well-founded Model semantics, generalizing many paraconsistent semantics for logic programs. In particular, Paraconsistent Well-Founded Semantics with eXplicit negation (WFSXp) [Alferes et al., J. Automated Reas. 14 (1) (1995) 93–147; Damásio, PhD thesis, 1996]. The framework is an extension of Antitonic Logic Programs for most cases, and is general enough to capture Probabilistic Deductive Databases, Possibilistic Logic Programming, Hybrid Probabilistic Logic Programs, and Fuzzy Logic Programming. Thus, we have a powerful mathematical formalism for dealing simultaneously with default, paraconsistency, and uncertainty reasoning. Results are provided about how our semantical framework deals with inconsistent information and with its propagation by the rules of the program
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