491,107 research outputs found
Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving
Behavior and motion planning play an important role in automated driving.
Traditionally, behavior planners instruct local motion planners with predefined
behaviors. Due to the high scene complexity in urban environments,
unpredictable situations may occur in which behavior planners fail to match
predefined behavior templates. Recently, general-purpose planners have been
introduced, combining behavior and local motion planning. These general-purpose
planners allow behavior-aware motion planning given a single reward function.
However, two challenges arise: First, this function has to map a complex
feature space into rewards. Second, the reward function has to be manually
tuned by an expert. Manually tuning this reward function becomes a tedious
task. In this paper, we propose an approach that relies on human driving
demonstrations to automatically tune reward functions. This study offers
important insights into the driving style optimization of general-purpose
planners with maximum entropy inverse reinforcement learning. We evaluate our
approach based on the expected value difference between learned and
demonstrated policies. Furthermore, we compare the similarity of human driven
trajectories with optimal policies of our planner under learned and
expert-tuned reward functions. Our experiments show that we are able to learn
reward functions exceeding the level of manual expert tuning without prior
domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote,
minor correction in preliminarie
QCD Sum Rules and Compton Scattering
We extend QCD sum rule analysis to moderate energy fixed angle Compton
scattering. In this kinematic region there is a strong similarity to the sum
rule treatment of electromagnetic form factors, although the four-point
amplitude requires a modification of the Borel transform. To illustrate our
method, we derive the sum rules for helicity amplitudes in pion Compton
scattering and estimate their large- behavior in the local duality
approximation.Comment: 30 pages in Latex, 6 figures not included, available upon request
(send email to: [email protected]), ITP-SB-92-70, CEBAF-TH-92-3
Local properties of extended self-similarity in 3D turbulence
Using a generalization of extended self-similarity we have studied local
scaling properties of 3D turbulence in a direct numerical simulation. We have
found that these properties are consistent with lognormal-like behavior of
energy dissipation fluctuations with moderate amplitudes for space scales
beginning from Kolmogorov length up to the largest scales, and in the
whole range of the Reynolds numbers: . The
locally determined intermittency exponent varies with ; it has a
maximum at scale , independent of .Comment: 4 pages, 5 figure
Tensor-Based Link Prediction in Intermittently Connected Wireless Networks
Through several studies, it has been highlighted that mobility patterns in
mobile networks are driven by human behaviors. This effect has been
particularly observed in intermittently connected networks like DTN (Delay
Tolerant Networks). Given that common social intentions generate similar human
behavior, it is relevant to exploit this knowledge in the network protocols
design, e.g. to identify the closeness degree between two nodes. In this paper,
we propose a temporal link prediction technique for DTN which quantifies the
behavior similarity between each pair of nodes and makes use of it to predict
future links. Our prediction method keeps track of the spatio-temporal aspects
of nodes behaviors organized as a third-order tensor that aims to records the
evolution of the network topology. After collapsing the tensor information, we
compute the degree of similarity for each pair of nodes using the Katz measure.
This metric gives us an indication on the link occurrence between two nodes
relying on their closeness. We show the efficiency of this method by applying
it on three mobility traces: two real traces and one synthetic trace. Through
several simulations, we demonstrate the effectiveness of the technique
regarding another approach based on a similarity metric used in DTN. The
validity of this method is proven when the computation of score is made in a
distributed way (i.e. with local information). We attest that the tensor-based
technique is effective for temporal link prediction applied to the
intermittently connected networks. Furthermore, we think that this technique
can go beyond the realm of DTN and we believe this can be further applied on
every case of figure in which there is a need to derive the underlying social
structure of a network of mobile users.Comment: 13 pages, 9 figures, 8 tables, submitted to the International Journal
of Computer and Telecommunications Networking (COMNET
Tracking Topology Dynamicity for Link Prediction in Intermittently Connected Wireless Networks
Through several studies, it has been highlighted that mobility patterns in
mobile networks are driven by human behaviors. This effect has been
particularly observed in intermittently connected networks like DTN (Delay
Tolerant Networks). Given that common social intentions generate similar human
behavior, it is relevant to exploit this knowledge in the network protocols
design, e.g. to identify the closeness degree between two nodes. In this paper,
we propose a temporal link prediction technique for DTN which quantifies the
behavior similarity between each pair of nodes and makes use of it to predict
future links. We attest that the tensor-based technique is effective for
temporal link prediction applied to the intermittently connected networks. The
validity of this method is proved when the prediction is made in a distributed
way (i.e. with local information) and its performance is compared to well-known
link prediction metrics proposed in the literature.Comment: Published in the proceedings of the 8th International Wireless
Communications and Mobile Computing Conference (IWCMC), Limassol, Cyprus,
201
Clustering of solutions in hard satisfiability problems
We study the structure of the solution space and behavior of local search
methods on random 3-SAT problems close to the SAT/UNSAT transition. Using the
overlap measure of similarity between different solutions found on the same
problem instance we show that the solution space is shrinking as a function of
alpha. We consider chains of satisfiability problems, where clauses are added
sequentially. In each such chain, the overlap distribution is first smooth, and
then develops a tiered structure, indicating that the solutions are found in
well separated clusters. On chains of not too large instances, all solutions
are eventually observed to be in only one small cluster before vanishing. This
condensation transition point is estimated to be alpha_c = 4.26. The transition
approximately obeys finite-size scaling with an apparent critical exponent of
about 1.7. We compare the solutions found by a local heuristic, ASAT, and the
Survey Propagation algorithm up to alpha_c.Comment: 8 pages, 9 figure
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