The Synergy of Finite State Machines

What can be computed by a network of n randomized finite state machines communicating under the stone age model (Emek & Wattenhofer, PODC 2013)? The inherent linear upper bound on the total space of the network implies that its global computational power is not larger than that of a randomized linear space Turing machine, but is this tight? We answer this question affirmatively for bounded degree networks by introducing a stone age algorithm (operating under the most restrictive form of the model) that given a designated I/O node, constructs a tour in the network that enables the simulation of the Turing machine\u27s tape. To construct the tour with high probability, we first show how to 2-hop color the network concurrently with building a spanning tree

Deterministic Treasure Hunt in the Plane with Angular Hints

International audienceA mobile agent equipped with a compass and a measure of length has to find an inert treasure in the Euclidean plane. Both the agent and the treasure are modeled as points. In the beginning, the agent is at a distance at most D>0 from the treasure, but knows neither the distance nor any bound on it. Finding the treasure means getting at distance at most 1 from it. The agent makes a series of moves. Each of them consists in moving straight in a chosen direction at a chosen distance. In the beginning and after each move the agent gets a hint consisting of a positive angle smaller than 2π whose vertex is at the current position of the agent and within which the treasure is contained. We investigate the problem of how these hints permit the agent to lower the cost of finding the treasure, using a deterministic algorithm, where the cost is the worst-case total length of the agent’s trajectory. It is well known that without any hint the optimal (worst case) cost is Θ(D2). We show that if all angles given as hints are at most π, then the cost can be lowered to O(D), which is the optimal complexity. If all angles are at most β, where β0. For both these positive results we present deterministic algorithms achieving the above costs. Finally, if angles given as hints can be arbitrary, smaller than 2π, then we show that cost complexity Θ(D2) cannot be beaten

Overcoming Obstacles with Ants

Consider a group of mobile finite automata, referred to as agents, located in the origin of an infinite grid. The grid is occupied by obstacles, i.e., sets of cells that can not be entered by the agents. In every step, an agent can sense the states of the co-located agents and is allowed to move to any neighboring cell of the grid not blocked by an obstacle. We assume that the circumference of each obstacle is finite but allow the number of obstacles to be unbounded. The task of the agents is to cooperatively find a treasure, hidden in the grid by an adversary. In this work, we show how the agents can utilize their simple means of communication and their constant memory to systematically explore the grid and to locate the treasure in finite time. As integral part of the agents\u27 behavior, we present a method that allows a group of six agents to follow a straight line, even if the line is partially obstructed by obstacles, and to discover all free cells along this line. In total, our search protocol requires nine agents