2,635 research outputs found
Graph Subsumption in Abstract State Space Exploration
In this paper we present the extension of an existing method for abstract
graph-based state space exploration, called neighbourhood abstraction, with a
reduction technique based on subsumption. Basically, one abstract state
subsumes another when it covers more concrete states; in such a case, the
subsumed state need not be included in the state space, thus giving a
reduction. We explain the theory and especially also report on a number of
experiments, which show that subsumption indeed drastically reduces both the
state space and the resources (time and memory) needed to compute it.Comment: In Proceedings GRAPHITE 2012, arXiv:1210.611
A Survey of Symbolic Execution Techniques
Many security and software testing applications require checking whether
certain properties of a program hold for any possible usage scenario. For
instance, a tool for identifying software vulnerabilities may need to rule out
the existence of any backdoor to bypass a program's authentication. One
approach would be to test the program using different, possibly random inputs.
As the backdoor may only be hit for very specific program workloads, automated
exploration of the space of possible inputs is of the essence. Symbolic
execution provides an elegant solution to the problem, by systematically
exploring many possible execution paths at the same time without necessarily
requiring concrete inputs. Rather than taking on fully specified input values,
the technique abstractly represents them as symbols, resorting to constraint
solvers to construct actual instances that would cause property violations.
Symbolic execution has been incubated in dozens of tools developed over the
last four decades, leading to major practical breakthroughs in a number of
prominent software reliability applications. The goal of this survey is to
provide an overview of the main ideas, challenges, and solutions developed in
the area, distilling them for a broad audience.
The present survey has been accepted for publication at ACM Computing
Surveys. If you are considering citing this survey, we would appreciate if you
could use the following BibTeX entry: http://goo.gl/Hf5FvcComment: This is the authors pre-print copy. If you are considering citing
this survey, we would appreciate if you could use the following BibTeX entry:
http://goo.gl/Hf5Fv
Bridging the gap between folksonomies and the semantic web: an experience report
Abstract. While folksonomies allow tagging of similar resources with a variety of tags, their content retrieval mechanisms are severely hampered by being agnostic to the relations that exist between these tags. To overcome this limitation, several methods have been proposed to find groups of implicitly inter-related tags. We believe that content retrieval can be further improved by making the relations between tags explicit. In this paper we propose the semantic enrichment of folksonomy tags with explicit relations by harvesting the Semantic Web, i.e., dynamically selecting and combining relevant bits of knowledge from online ontologies. Our experimental results show that, while semantic enrichment needs to be aware of the particular characteristics of folksonomies and the Semantic Web, it is beneficial for both.
Improving search order for reachability testing in timed automata
Standard algorithms for reachability analysis of timed automata are sensitive
to the order in which the transitions of the automata are taken. To tackle this
problem, we propose a ranking system and a waiting strategy. This paper
discusses the reason why the search order matters and shows how a ranking
system and a waiting strategy can be integrated into the standard reachability
algorithm to alleviate and prevent the problem respectively. Experiments show
that the combination of the two approaches gives optimal search order on
standard benchmarks except for one example. This suggests that it should be
used instead of the standard BFS algorithm for reachability analysis of timed
automata
Model Checker Execution Reports
Software model checking constitutes an undecidable problem and, as such, even
an ideal tool will in some cases fail to give a conclusive answer. In practice,
software model checkers fail often and usually do not provide any information
on what was effectively checked. The purpose of this work is to provide a
conceptual framing to extend software model checkers in a way that allows users
to access information about incomplete checks. We characterize the information
that model checkers themselves can provide, in terms of analyzed traces, i.e.
sequences of statements, and safe cones, and present the notion of execution
reports, which we also formalize. We instantiate these concepts for a family of
techniques based on Abstract Reachability Trees and implement the approach
using the software model checker CPAchecker. We evaluate our approach
empirically and provide examples to illustrate the execution reports produced
and the information that can be extracted
Q Learning Behavior on Autonomous Navigation of Physical Robot
Behavior based architecture gives robot fast and reliable action. If there are many behaviors in robot, behavior coordination is needed. Subsumption architecture is behavior coordination method that give quick and robust response. Learning mechanism improve robot’s performance in handling uncertainty. Q learning is popular reinforcement learning method that has been used in robot learning because it is simple, convergent and off
policy. In this paper, Q learning will be used as learning mechanism for obstacle avoidance behavior in autonomous robot navigation. Learning rate of Q learning affect robot’s performance in learning phase. As the result,
Q learning algorithm is successfully implemented in a physical robot with its imperfect environment
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