113,781 research outputs found
Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning
In this work we propose a coverage planning control approach which allows a
mobile agent, equipped with a controllable sensor (i.e., a camera) with limited
sensing domain (i.e., finite sensing range and angle of view), to cover the
surface area of an object of interest. The proposed approach integrates
ray-tracing into the coverage planning process, thus allowing the agent to
identify which parts of the scene are visible at any point in time. The problem
of integrated ray-tracing and coverage planning control is first formulated as
a constrained optimal control problem (OCP), which aims at determining the
agent's optimal control inputs over a finite planning horizon, that minimize
the coverage time. Efficiently solving the resulting OCP is however very
challenging due to non-convex and non-linear visibility constraints. To
overcome this limitation, the problem is converted into a Markov decision
process (MDP) which is then solved using reinforcement learning. In particular,
we show that a controller which follows an optimal control law can be learned
using off-policy temporal-difference control (i.e., Q-learning). Extensive
numerical experiments demonstrate the effectiveness of the proposed approach
for various configurations of the agent and the object of interest.Comment: 2022 IEEE 61st Conference on Decision and Control (CDC), 06-09
December 2022, Cancun, Mexic
On Uniformly Sampling Traces of a Transition System (Extended Version)
A key problem in constrained random verification (CRV) concerns generation of
input stimuli that result in good coverage of the system's runs in targeted
corners of its behavior space. Existing CRV solutions however provide no formal
guarantees on the distribution of the system's runs. In this paper, we take a
first step towards solving this problem. We present an algorithm based on
Algebraic Decision Diagrams for sampling bounded traces (i.e. sequences of
states) of a sequential circuit with provable uniformity (or bias) guarantees,
while satisfying given constraints. We have implemented our algorithm in a tool
called TraceSampler. Extensive experiments show that TraceSampler outperforms
alternative approaches that provide similar uniformity guarantees.Comment: Extended version of paper that will appear in proceedings of
International Conference on Computer-Aided Design (ICCAD '20); changed wrong
text color in sec 7; added 'extended version
Diverse Weighted Bipartite b-Matching
Bipartite matching, where agents on one side of a market are matched to
agents or items on the other, is a classical problem in computer science and
economics, with widespread application in healthcare, education, advertising,
and general resource allocation. A practitioner's goal is typically to maximize
a matching market's economic efficiency, possibly subject to some fairness
requirements that promote equal access to resources. A natural balancing act
exists between fairness and efficiency in matching markets, and has been the
subject of much research.
In this paper, we study a complementary goal---balancing diversity and
efficiency---in a generalization of bipartite matching where agents on one side
of the market can be matched to sets of agents on the other. Adapting a
classical definition of the diversity of a set, we propose a quadratic
programming-based approach to solving a supermodular minimization problem that
balances diversity and total weight of the solution. We also provide a scalable
greedy algorithm with theoretical performance bounds. We then define the price
of diversity, a measure of the efficiency loss due to enforcing diversity, and
give a worst-case theoretical bound. Finally, we demonstrate the efficacy of
our methods on three real-world datasets, and show that the price of diversity
is not bad in practice
A Strategy Language for Testing Register Transfer Level Logic
The development of modern ICs requires a huge investment in RTL verification.
This is a reflection of brisk release schedules and the complexity of
contemporary chip designs. A major bottleneck to reaching verification closure
in such designs is the disproportionate effort expended in crafting directed
tests; which is necessary to reach those behaviors that other, more automated
testing methods fail to cover. This paper defines a novel language that can be
used to generate targeted stimuli for RTL logic and which mitigates the
complexities of writing directed tests. The main idea is to treat directed
testing as a meta-reasoning problem about simulation. Our language is both
formalized and prototyped as a proof-search strategy language in rewriting
logic. We illustrate its novel features and practical use with several
examples.published or submitted for publicatio
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