SA
S.J.L. Adams
4 records found
1
Authored
In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of adversarial robustness of Bayesian Neural Networks (BNNs). Given a compact set of input points T ⊂ Rn, BNN-DP computes lower and upper bounds on the BNN's predictions for all the ...
We present a biologically inspired design for swarm foraging based on ant’s pheromone deployment, where the swarm is assumed to have very restricted capabilities. The robots do not require global or relative position measurements and the swarm is fully decentralized and needs ...
Neural networks (NNs) are emerging as powerful tools to represent the dynamics of control systems with complicated physics or black-box components. Due to complexity of NNs, however, existing methods are unable to synthesize complex behaviors with guarantees for NN dynamic models
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Contributed
Formal Control of an Inverted Pendulum on a Cart via Stochastic Abstractions
Using Interval Markov Decision Processes and Linear Temporal Logic on Finite Traces
The use of machine learning (ML), especially neural networks, in modeling control systems has shown promise, particularly for systems with complex physics. However, applying these models in safety-critical areas requires reliable verification and control synthesis methods due to
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