Search for an Immobile Hider on a Stochastic Network

Harry hides on an edge of a graph and does not move from there. Sally, starting from a known origin, tries to find him as soon as she can. Harry's goal is to be found as late as possible. At any given time, each edge of the graph is either active or inactive, independently of the other edges, with a known probability of being active. This situation can be modeled as a zero-sum two-person stochastic game. We show that the game has a value and we provide upper and lower bounds for this value. Finally, by generalizing optimal strategies of the deterministic case, we provide more refined results for trees and Eulerian graphs.Comment: 28 pages, 9 figure

Search for an immobile Hider in a known subset of a network

A unit speed Searcher, constrained to start in a given closed set S, wishes to quickly find a point x known to be located in a given closed subset H of a metric network Q. This defines a game G=G(Q,H,S), where the payoff to the maximizing Hider is the time for the Searcher path to reach x. Lengths on Q are defined by a measure λ, which then defines distance as least length of connecting path. For trees Q, we find that the minimax search time (value V of G) is given by V=λ(H)-d_{H}(S)/2, where d_{H}(S) is what we call the `H-diameter of S', and equals the usual diameter d(S) of S in the case H=Q. For the classical case of Gal where the S is a singleton and H=Q, our formula reduces to his result V=λ(Q). If S=H=Q, our formula gives Dagan and Gal's result V=λ(Q)-d(Q)/2. In all other cases, our result is new. Optimal searches consist of minimum length paths covering H which start and end at points of S, traversed equiprobably in either direction

Hide-and-seek games on a network, using combinatorial search paths

This paper introduces a new search paradigm to hide-and-seek games on networks. The Hider locates at any point on any arc. The Searcher adopts a “combinatorial” path when searching the network: a sequence of arcs, each adjacent to the last, and traced out at unit speed. In previous literature the Searcher was allowed “simple motion,” any unit speed path, including ones that turn around inside an arc. The new approach more closely models real problems such as search for improvised explosive devices using vehicles that can only turn around at particular locations on a road. The search game is zero sum, with the time taken to find the Hider as the payoff. Using a lemma giving an upper bound for the expected search time on a semi Eulerian network, we solve the search game on a network Q3 consisting of two nodes connected by three arcs of arbitrary lengths. When two Q3 networks with unit length arcs are linked by two small central arcs incident at the start node, one of these arcs must be traversed at least three times in an optimal search. This property holds for both combinatorial paths and simple motion paths, and the latter makes it a counterexample to a conjecture of Gal, which said that two traversals were always sufficient

The expanding search ratio of a graph

We study the problem of searching for a hidden target in an environment that is modeled by an edge-weighted graph. Most of the previous work on this problem considers the pathwise cost formulation, in which the cost incurred by the searcher is the overall time to locate the target, assuming that the searcher moves at unit speed. More recent work introduced the setting of expanding search in which the searcher incurs cost only upon visiting previously unexplored areas of the graph. Such a paradigm is useful in modeling problems in which the cost of re-exploration is negligible (such as coal mining). In our work we study algorithmic and computational issues of expanding search, for a variety of search environments including general graphs, trees and star-like graphs. In particular, we rely on the deterministic and randomized search ratio as the performance measures of search strategies, which were originally introduced by Koutsoupias and Papadimitriou [ICALP 1996] in the context of pathwise search. The search ratio is essentially the best competitive ratio among all possible strategies. Our main objective is to explore how the transition from pathwise to expanding search affects the competitive analysis, which has applications to optimization problems beyond the strict boundaries of search problems

Searching a variable speed network

A point lies on a network according to some unknown probability distribution. Starting at a specified root of the network, a Searcher moves to find this point at speeds that depend on his location and direction. He seeks the randomized search algorithm that minimizes the expected search time. This is equivalent to modeling the problem as a zero-sum hide-and-seek game whose value is called the search value of the network. We make a new and direct derivation of an explicit formula for the search value of a tree, proving that it is equal to half the sum of the minimum tour time of the tree and a quantity called its incline. The incline of a tree is an average over the leaf nodes of the difference between the time taken to travel from the root to a leaf node and the time taken to travel from a leaf node to the root. This difference can be interpreted as height of a leaf node, assuming uphill is slower than downhill. We then apply this formula to obtain numerous results for general networks. We also introduce a new general method of comparing the search value of networks that differ in a single arc. Some simple networks have very complicated optimal strategies that require mixing of a continuum of pure strategies. Many of our results generalize analogous ones obtained for constant velocity (in both directions) by S. Gal, but not all of those results can be extended

Optimizing periodic patrols against short attacks on the line and other networks

On a given network, a Patroller and Attacker play the following win-lose game: The Patroller adopts a periodic walk on the network while the Attacker chooses a node and two consecutive periods (to attack there). The Patroller wins if he successfully intercepts the attack, that is, if he occupies the attacked node in one of the two periods of the attack. We solve this game in mixed strategies for line graphs, the first class of graphs to be solved for the periodic patrolling game. We also solve the game for arbitrary graphs when the period is even