96 research outputs found
Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions
It is well-known that any admissible unidirectional heuristic search
algorithm must expand all states whose -value is smaller than the optimal
solution cost when using a consistent heuristic. Such states are called "surely
expanded" (s.e.). A recent study characterized s.e. pairs of states for
bidirectional search with consistent heuristics: if a pair of states is s.e.
then at least one of the two states must be expanded. This paper derives a
lower bound, VC, on the minimum number of expansions required to cover all s.e.
pairs, and present a new admissible front-to-end bidirectional heuristic search
algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no
more than 2VC expansions. We further prove that no admissible front-to-end
algorithm has a worst case better than 2VC. Experimental results show that NBS
competes with or outperforms existing bidirectional search algorithms, and
often outperforms A* as well.Comment: Accepted to IJCAI 2017. Camera ready version with new timing result
Inferring Symbolic Automata
We study the learnability of symbolic finite state automata, a model shown useful in many applications in software verification. The state-of-the-art literature on this topic follows the query learning paradigm, and so far all obtained results are positive. We provide a necessary condition for efficient learnability of SFAs in this paradigm, from which we obtain the first negative result. The main focus of our work lies in the learnability of SFAs under the paradigm of identification in the limit using polynomial time and data. We provide a necessary condition and a sufficient condition for efficient learnability of SFAs in this paradigm, from which we derive a positive and a negative result
An Overview of Machine Teaching
In this paper we try to organize machine teaching as a coherent set of ideas.
Each idea is presented as varying along a dimension. The collection of
dimensions then form the problem space of machine teaching, such that existing
teaching problems can be characterized in this space. We hope this organization
allows us to gain deeper understanding of individual teaching problems,
discover connections among them, and identify gaps in the field.Comment: A tutorial document grown out of NIPS 2017 Workshop on Teaching
Machines, Robots, and Human
Inferring Symbolic Automata
We study the learnability of symbolic finite state automata, a model shown useful in many applications in software verification. The state-of-the-art literature on this topic follows the query learning paradigm, and so far all obtained results are positive. We provide a necessary condition for efficient learnability of SFAs in this paradigm, from which we obtain the first negative result. The main focus of our work lies in the learnability of SFAs under the paradigm of identification in the limit using polynomial time and data. We provide a necessary condition and a sufficient condition for efficient learnability of SFAs in this paradigm, from which we derive a positive and a negative result
On the barrier-resilience of arrangements of ray-sensors
Given an arrangement A of n sensors and two points s and t in the plane, the barrier resilience of A with respect to s and t is the minimum number
of sensors whose removal permits a path from s to t such that the path does not intersect the coverage region of any sensor in A. When the surveillance domain is the entire plane and sensor coverage regions are unit line segments, even with restricted orientations, the problem of determining
the barrier resilience is known to be NP-hard. On the other hand, if sensor coverage regions are arbitrary lines, the problem has a trivial linear time solution. In this paper, we give an O(n2m) time algorithm for computing
the barrier resilience when each sensor coverage region is an arbitrary ray, where m is the number of sensor intersections.Natural Sciences and Engineering Research Council of Canad
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