876 research outputs found
LTLf satisfiability checking
We consider here Linear Temporal Logic (LTL) formulas interpreted over
\emph{finite} traces. We denote this logic by LTLf. The existing approach for
LTLf satisfiability checking is based on a reduction to standard LTL
satisfiability checking. We describe here a novel direct approach to LTLf
satisfiability checking, where we take advantage of the difference in the
semantics between LTL and LTLf. While LTL satisfiability checking requires
finding a \emph{fair cycle} in an appropriate transition system, here we need
to search only for a finite trace. This enables us to introduce specialized
heuristics, where we also exploit recent progress in Boolean SAT solving. We
have implemented our approach in a prototype tool and experiments show that our
approach outperforms existing approaches
Fast LTL Satisfiability Checking by SAT Solvers
Satisfiability checking for Linear Temporal Logic (LTL) is a fundamental step
in checking for possible errors in LTL assertions. Extant LTL satisfiability
checkers use a variety of different search procedures. With the sole exception
of LTL satisfiability checking based on bounded model checking, which does not
provide a complete decision procedure, LTL satisfiability checkers have not
taken advantage of the remarkable progress over the past 20 years in Boolean
satisfiability solving. In this paper, we propose a new LTL
satisfiability-checking framework that is accelerated using a Boolean SAT
solver. Our approach is based on the variant of the \emph{obligation-set
method}, which we proposed in earlier work. We describe here heuristics that
allow the use of a Boolean SAT solver to analyze the obligations for a given
LTL formula. The experimental evaluation indicates that the new approach
provides a a significant performance advantage
Vehicle Speed Aware Computing Task Offloading and Resource Allocation Based on Multi-Agent Reinforcement Learning in a Vehicular Edge Computing Network
For in-vehicle application, the vehicles with different speeds have different
delay requirements. However, vehicle speeds have not been extensively explored,
which may cause mismatching between vehicle speed and its allocated computation
and wireless resource. In this paper, we propose a vehicle speed aware task
offloading and resource allocation strategy, to decrease the energy cost of
executing tasks without exceeding the delay constraint. First, we establish the
vehicle speed aware delay constraint model based on different speeds and task
types. Then, the delay and energy cost of task execution in VEC server and
local terminal are calculated. Next, we formulate a joint optimization of task
offloading and resource allocation to minimize vehicles' energy cost subject to
delay constraints. MADDPG method is employed to obtain offloading and resource
allocation strategy. Simulation results show that our algorithm can achieve
superior performance on energy cost and task completion delay.Comment: 8 pages, 6 figures, Accepted by IEEE International Conference on Edge
Computing 202
Mining Recent Frequent Itemsets in Sliding Windows over Data Streams
This paper considers the problem of mining recent frequent itemsets over data streams. As the data grows without limit at a rapid rate, it is hard to track the new changes of frequent itemsets over data streams. We propose an efficient one-pass algorithm in sliding windows over data streams with an error bound guarantee. This algorithm does not need to refer to obsolete transactions when they are removed from the sliding window. It exploits a compact data structure to maintain potentially frequent itemsets so that it can output recent frequent itemsets at any time. Flexible queries for continuous transactions in the sliding window can be answered with an error bound guarantee
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