  We study search games between a mobile Searcher and an immobile Hider in
which the Searcher aims to minimize some payoff, which is either the time to
find the Hider (the search time), or a normalized search time. We consider a
new setting in which the Searcher has some potentially erroneous information,
or prediction on the Hider's position. Specifically, we study tradeoffs between
the consistency of a search strategy (i.e., its worst case expected payoff
assuming the prediction is correct) and the robustness (i.e., the worst case
expected payoff assuming that the prediction is adversarially generated). We
show how to apply this framework in search games over both discrete and
continuous, as well as bounded and unbounded spaces. Specifically, we prove
optimal consistency/robustness tradeoffs for three fundamental search games,
namely searching in a number of discrete locations, expanding search in a tree
network, and searching in the infinite line. Our study is the first to address
the full power of mixed (randomized) strategies; previous work focused only on
deterministic strategies, or relied on stochastic assumptions that do not
guarantee worst-case robustness in adversarial situations.
