Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Saisubramanian, Sandhya"'
When independently trained or designed robots are deployed in a shared environment, their combined actions can lead to unintended negative side effects (NSEs). To ensure safe and efficient operation, robots must optimize task performance while minimi
Externí odkaz:
http://arxiv.org/abs/2405.04702
Agents operating in unstructured environments often produce negative side effects (NSE), which are difficult to identify at design time. While the agent can learn to mitigate the side effects from human feedback, such feedback is often expensive and
Externí odkaz:
http://arxiv.org/abs/2102.07017
Fair clustering is the process of grouping similar entities together, while satisfying a mathematically well-defined fairness metric as a constraint. Due to the practical challenges in precise model specification, the prescribed fairness constraints
Externí odkaz:
http://arxiv.org/abs/2102.03977
Autonomous agents acting in the real-world often operate based on models that ignore certain aspects of the environment. The incompleteness of any given model -- handcrafted or machine acquired -- is inevitable due to practical limitations of any mod
Externí odkaz:
http://arxiv.org/abs/2008.12146
Graph clustering groups entities -- the vertices of a graph -- based on their similarity, typically using a complex distance function over a large number of features. Successful integration of clustering approaches in automated decision-support syste
Externí odkaz:
http://arxiv.org/abs/1912.07820
Reduced models of large Markov decision processes accelerate planning by considering a subset of outcomes for each state-action pair. This reduction in reachable states leads to replanning when the agent encounters states without a precomputed action
Externí odkaz:
http://arxiv.org/abs/1905.09355
We introduce a rich model for multi-objective clustering with lexicographic ordering over objectives and a slack. The slack denotes the allowed multiplicative deviation from the optimal objective value of the higher priority objective to facilitate i
Externí odkaz:
http://arxiv.org/abs/1903.00750
Publikováno v:
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019, pp. 1649-1654
We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem -- a general framework to model path planning and decision making in stochastic environments with goal uncertainty. The framework extends the stochastic shortest path (SSP) model
Externí odkaz:
http://arxiv.org/abs/1810.08159
Autor:
Saisubramanian, Sandhya
The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d311e6e86395df7c12773ddd843de28d
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