Author name cluster

Ofri Keidar

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

2 author rows

AAMAS Conference 2017 Conference Paper

Safety First: Strategic Navigation in Adversarial Environments

Ofri Keidar
Noa Agmon

This work deals with the problem of navigation while avoiding detection by a mobile adversary, which is a novel variant of pursuit-evasion featuring adversarial modeling. In this problem, an evading agent is placed on a graph, where one or more nodes are defined as safehouses. The agent’s goal is to find a path from its current location to a safehouse, while minimizing the probability of meeting a mobile adversarial agent at a node along its path (i. e. , being captured). We examine several models of this problem, where each one has different assumptions on what the agents know about their opponent, all using a framework for computing node utility, introduced herein. We use several risk attitudes for computing the utility values, whose impact on the constructed strategies is analyzed both theoretically and empirically. Furthermore, we allow the agents to use information gained along their movement, in order to efficiently update their motion strategies on-the-fly. Analytic and empirical analysis show the importance of using this information and these on-the-fly strategy updates.

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ECAI Conference 2016 Conference Paper

Strategic Path Planning Allowing on-the-Fly Updates

Ofri Keidar
Noa Agmon

This work deals with the problem of strategic path planning while avoiding detection by a mobile adversary. In this problem, an evading agent is placed on a graph, where one or more nodes are defined as safehouses. The agent's goal is to find a path from its current location to a safehouse, while minimizing the probability of meeting a mobile adversarial agent at a node along its path (i. e. , being captured). We examine several models of this problem, where each one has different assumptions on what the agents know about their opponent, all using a framework for computing node utility. We use several risk attitudes for computing the utility values, whose impact on the actual performance of the path planning algorithms is highlighted by an empirical analysis. Furthermore, we allow the agents to use information gained along their movement, in order to efficiently update their motion strategies on-the-fly. Analytic and empiric analysis show that on-the-fly updates increase the probability that our agent reaches its destination safely.

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