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A mobile agent navigating along edges of a simple connected unweighted graph, either finite or countably infinite, has to find an inert target (treasure) hidden in one of the nodes. This task is known as treasure hunt. The agent has no a priori knowledge of the graph, of the location of the treasure, or of the initial distance to it. The cost of a treasure hunt algorithm is the worst-case number of edge traversals performed by the agent until finding the treasure. Awerbuch et al. [3] considered graph exploration and treasure hunt for finite graphs in a restricted model where the agent has a fuel tank that can be replenished only at the starting node s. The size of the tank is B = 2(1 + a)r, for some positive real constant a, where r, called the radius of the graph, is the maximum distance from s to any other node. The tank of size B allows the agent to make at most -B° edge traversals between two consecutive visits at node s. Let e (d) be the number of edges whose at least one endpoint is at distance less than d from s. Awerbuch et al. [3] conjectured that it is impossible to find a treasure hidden in a node at distance at most d at cost nearly linear in e (d). We first design a deterministic treasure hunt algorithm working in the model without any restrictions on the moves of the agent at cost O(e (d) logd) and then show how to modify this algorithm to work in the model from Awerbuch et al. [3] with the same complexity. Thus, we refute the preceding 20-year-old conjecture. We observe that no treasure hunt algorithm can beat cost T(e (d)) for all graphs, and thus our algorithms are also almost optimal. [ABSTRACT FROM AUTHOR]

Treasure hunt is finding a hidden inert target by a mobile agent. We consider deterministic algorithms for treasure hunt in trees. Our goal is to establish the impact of different kinds of initial knowledge given to the agent on the cost of treasure hunt, defined as the total number of edge traversals until the agent reaches the treasure. The agent can be initially given either a complete map of the tree rooted at its starting node, with all port numbers marked, or a blind map of the tree rooted at its starting node but without port numbers. It may also be given, or not, the distance from the root to the treasure. This yields four different knowledge types that are partially ordered by their precision. The penalty of a less precise knowledge type 풯2 over a more precise knowledge type 풯1 measures intuitively the worst‐case ratio of the cost of an algorithm supplied with knowledge of type 풯2 over the cost of an algorithm supplied with knowledge of type 풯1. Our main results establish penalties for comparable knowledge types in this partial order. For knowledge types with known distance, the penalty for having a blind map over a complete map turns out to be very large. By contrast, for unknown distance, the penalty of having a blind map over having a complete map is small. When a map is provided (either complete or blind), the penalty of not knowing the distance over knowing it is medium. [ABSTRACT FROM AUTHOR]

Abstract: Two mobile agents (robots) have to meet in an a priori unknown bounded terrain modeled as a polygon, possibly with polygonal obstacles. Robots are modeled as points, and each of them is equipped with a compass. Compasses of robots may be incoherent. Robots construct their routes, but the actual walk of each robot is decided by the adversary that may, e.g., speed up or slow down the robot. We consider several scenarios, depending on three factors: (1) obstacles in the terrain are present, or not, (2) compasses of both robots agree, or not, (3) robots have or do not have a map of the terrain with their positions marked. The cost of a rendezvous algorithm is the worst-case sum of lengths of the robots’ trajectories until they meet. For each scenario, we design a deterministic rendezvous algorithm and analyze its cost. We also prove lower bounds on the cost of any deterministic rendezvous algorithm in each case. For all scenarios these bounds are tight. [Copyright &y& Elsevier]