23,908 research outputs found
Simulation Subsumption or DĂ©jĂ vu on the Web
Simulation unification is a special kind of unification adapted to retrieving semi-structured data on the Web. This article introduces simulation subsumption, or containment, that is, query subsumption under simulation unification. Simulation subsumption is crucial in general for query optimization, in particular for optimizing pattern-based search engines, and for the termination of recursive rule-based web languages such as the XML and RDF query language Xcerpt. This paper first motivates and formalizes simulation subsumption. Then, it establishes decidability of simulation subsumption for advanced query patterns featuring descendant constructs, regular expressions, negative subterms (or subterm exclusions), and multiple variable occurrences. Finally, we show that subsumption between two query terms can be decided in O(n!n) where n is the sum of the sizes of both query terms
One-Chip Solution to Intelligent Robot Control: Implementing Hexapod Subsumption Architecture Using a Contemporary Microprocessor
This paper introduces a six-legged autonomous robot managed by a single
controller and a software core modeled on subsumption architecture. We begin by
discussing the features and capabilities of IsoPod, a new processor for
robotics which has enabled a streamlined implementation of our project. We
argue that this processor offers a unique set of hardware and software
features, making it a practical development platform for robotics in general
and for subsumption-based control architectures in particular. Next, we
summarize original ideas on subsumption architecture implementation for a
six-legged robot, as presented by its inventor Rodney Brooks in 1980s. A
comparison is then made to a more recent example of a hexapod control
architecture based on subsumption. The merits of both systems are analyzed and
a new subsumption architecture layout is formulated as a response. We conclude
with some remarks regarding the development of this project as a hint at new
potentials for intelligent robot design, opened by a recent development in
embedded controller market
Learning for Dynamic subsumption
In this paper a new dynamic subsumption technique for Boolean CNF formulae is
proposed. It exploits simple and sufficient conditions to detect during
conflict analysis, clauses from the original formula that can be reduced by
subsumption. During the learnt clause derivation, and at each step of the
resolution process, we simply check for backward subsumption between the
current resolvent and clauses from the original formula and encoded in the
implication graph. Our approach give rise to a strong and dynamic
simplification technique that exploits learning to eliminate literals from the
original clauses. Experimental results show that the integration of our dynamic
subsumption approach within the state-of-the-art SAT solvers Minisat and Rsat
achieves interesting improvements particularly on crafted instances
Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video
We develop, analyze, and evaluate a novel, supervised, specific-to-general
learner for a simple temporal logic and use the resulting algorithm to learn
visual event definitions from video sequences. First, we introduce a simple,
propositional, temporal, event-description language called AMA that is
sufficiently expressive to represent many events yet sufficiently restrictive
to support learning. We then give algorithms, along with lower and upper
complexity bounds, for the subsumption and generalization problems for AMA
formulas. We present a positive-examples--only specific-to-general learning
method based on these algorithms. We also present a polynomial-time--computable
``syntactic'' subsumption test that implies semantic subsumption without being
equivalent to it. A generalization algorithm based on syntactic subsumption can
be used in place of semantic generalization to improve the asymptotic
complexity of the resulting learning algorithm. Finally, we apply this
algorithm to the task of learning relational event definitions from video and
show that it yields definitions that are competitive with hand-coded ones
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