Prasanna Kannappan

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In this work, we consider an agent playing a turnbased game in a known environment against an adversary with unknown dynamics. The model of the adversary is assumed to belong to a subclass of regular languages that can be learned in the limit. We use tools from formal methods to synthesize a control strategy for the agent to win the game as it learns the(More)
The paper demonstrates that aspects of resilience of supervisory Cyber-Physical Systems (CPSs) can be improved through the inclusion of appropriate learning modules in the subordinate autonomous agents. During normal operation, individual agents keep track of their supervisor's commands and utilize the learning module, based on Grammatical Inference, to(More)
Automating the counting of marine animals like scallops benefits marine population survey efforts. These surveys are tools for policy makers to regulate fishing activities, and sources of information for biologists and marine ecologists interested in population statistics of marine species. In this paper we discuss some practical difficulties that arise in(More)
The paper contributes to the design of secure and resilient supervisory Cyber-Physical Systems (CPS) through learning. The reported approach involves the inclusion of learning modules in each of the supervised agents, and considers a scenario where the system’s coordinator privately transmits to individual agents their action plans in the form of symbolic(More)
10 The paper presents an algorithmic framework for the automated analysis of benthic 11 imagery data. The data are collected by an autonomous underwater vehicle for the purpose 12 of population assessment of epibenthic organisms, such as scallops. The architecture consists 13 of three layers of processing: visual attention, graph-cut segmentation methods,(More)
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