Exploring an Infinite Space with Finite Memory Scouts

Consider a small number of scouts exploring the infinite $d$-dimensional grid with the aim of hitting a hidden target point. Each scout is controlled by a probabilistic finite automaton that determines its movement (to a neighboring grid point) based on its current state. The scouts, that operate under a fully synchronous schedule, communicate with each other (in a way that affects their respective states) when they share the same grid point and operate independently otherwise. Our main research question is: How many scouts are required to guarantee that the target admits a finite mean hitting time? Recently, it was shown that $d + 1$ is an upper bound on the answer to this question for any dimension $d \geq 1$ and the main contribution of this paper comes in the form of proving that this bound is tight for $d \in \{ 1, 2 \}$.Comment: Added (forgotten) acknowledgement

The power of a pebble : exploring and mapping directed graphs

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 36-39).by Amit Sahai.M.S

Tight bounds for undirected graph exploration with pebbles and multiple agents

We study the problem of deterministically exploring an undirected and initially unknown graph with $n$ vertices either by a single agent equipped with a set of pebbles, or by a set of collaborating agents. The vertices of the graph are unlabeled and cannot be distinguished by the agents, but the edges incident to a vertex have locally distinct labels. The graph is explored when all vertices have been visited by at least one agent. In this setting, it is known that for a single agent without pebbles $\Theta(\log n)$ bits of memory are necessary and sufficient to explore any graph with at most $n$ vertices. We are interested in how the memory requirement decreases as the agent may mark vertices by dropping and retrieving distinguishable pebbles, or when multiple agents jointly explore the graph. We give tight results for both questions showing that for a single agent with constant memory $\Theta(\log \log n)$ pebbles are necessary and sufficient for exploration. We further prove that the same bound holds for the number of collaborating agents needed for exploration. For the upper bound, we devise an algorithm for a single agent with constant memory that explores any $n$-vertex graph using $\mathcal{O}(\log \log n)$ pebbles, even when $n$ is unknown. The algorithm terminates after polynomial time and returns to the starting vertex. Since an additional agent is at least as powerful as a pebble, this implies that $\mathcal{O}(\log \log n)$ agents with constant memory can explore any $n$-vertex graph. For the lower bound, we show that the number of agents needed for exploring any graph of size $n$ is already $\Omega(\log \log n)$ when we allow each agent to have at most $\mathcal{O}( \log n ^{1-\varepsilon})$ bits of memory for any $\varepsilon>0$. This also implies that a single agent with sublogarithmic memory needs $\Theta(\log \log n)$ pebbles to explore any $n$-vertex graph

Поведение конечных автоматов в лабиринтах

The paper is devoted to the study of problems on the behavior of finite automata in mazes. For any n, a maze is constructed that can be bypassed with 2n stones but you can’t get around with n stones. The range of tasks is extensive and touches upon key aspects of theoretical Computer Science. Of course, the solution of such problems does not mean the automatic solution of complex problems of complexity theory, however, the consideration of these issues can have a positive impact on the understanding of the essence of theoretical Computer Science. It is hoped that the behavior of automata in mazes is a good model for non-trivial information theoretic problems, and the development of methods and approaches to the study of robot behavior will give more serious results in the future. Problems related to automaton analysis of geometric media have a rather rich history of study. The first work that gave rise to this kind of problems, it is necessary to recognize the work of Shannon [24]. It deals with a model of a mouse in the form of an automaton, which must find a specific target in the maze. Another early work, one way or another affecting our problems, is the work of Fisher [9] on computing systems with external memory in the form of a discrete plane. A serious impetus to the study of the behavior of automata in mazes was the work of Depp [7, 8], in which the following model is proposed: there is a certain configuration of cells from mathbbZ^2 (chess maze), in which finite automata, surveying some neighborhood of the cell in which they are, can move to an adjacent cell in one of four directions. The main question posed in such a model is whether there is an automaton that bypasses all such mazes. In [20], Muller constructed a flat trap for a given automaton (a maze that does not completely bypass) in the form of a 3-graph. Budach [5] constructed a chess trap for any given finite automaton. Note that Budach’s solution was quite complex (the first versions contained 175 pages). More visual solutions to this question are presented here [29, 31, 33, 34]. Antelman [2] estimated the complexity of such a trap by the number of cells, and in [1] Antelman, Budach, and Rollick made a finite trap for any finite automaton system. In the formulation with a chess maze and one automaton, there are a number of results related to the problems of traversability of labyrinths with different numbers of holes, with bundles of labyrinths by the number of States of the automaton, and other issues. An overview of such problems can be found for example here [35]. The impossibility of traversing all flat chess labyrinths with one automaton raised the question of studying the possible amplifications of the automaton model, which will solve the problem of traversal. The main way of strengthening can be the consideration of a collective of automata, instead of one automaton, interacting with each other. A special and widely used case is the consideration of a system of one full-fledged automaton and a certain number of automata of stones, which have no internal state and can move only together with the main automaton. Interaction between machines is a key feature of this gain, it is allowed to have a collective (or one machine with stones) external memory, thereby significantly diversifies its behavior. If you get rid of the interaction of automata, the resulting  independent system will be little better than a single machine. Next, we discuss the known results associated with the collective automata.Работа посвящена исследованию задач о поведении конечных автоматов в лабиринтах. Для любого n строится лабиринт, который можно обойти с помощью 2n камней но нельзя обойти с помощью n камней. Спектр задач обхода обширен и затрагивает ключевые аспекты теоретической Computer Science. Конечно, решение таких задач не означает автоматическое решение сложных проблем теории сложности, тем не менее рассмотрение данных вопросов может положительно сказаться на понимании сути теоретической Computer Science. Есть надежда, что поведение автоматов в лабиринтах является хорошей моделью для нетривиальных теоретико-информационных задач, и отработка методов и подходов к исследованию поведения роботов даст более серьезные результаты с будущем. Задачи связанные c автоматным анализом геометрических сред имеют довольно богатую историю изучения. Первой работой, давшей начало подобного рода задачам, стоит признать работу Шеннона [24]. В ней рассматривается модель мыши в виде автомата, которая должна найти определенную цель в лабиринте. Другая ранняя работа, так или иначе затрагивающая нашу проблематику, это работа Фишера [9] о вычислительных системах с внешней памятью в виде дискретной плоскости. Серьёзным толчком к исследование поведения автоматов в лабиринтах послужила работы Деппа [7, 8], в которых предложена следующая модель: имеется некоторая конфигурация клеток из Z^2 (шахматный лабиринт), в которой конечные автоматы, обозревая некоторую окрестность клетки, в которой они находятся, могут перемещаться в соседнюю клетку в одном из четырёх направлений. Основной вопрос, который ставится в подобной модели, существует ли автомат обходящий все подобные лабиринты. В [20] Мюллер построил для заданного автомата плоскую ловушку (лабиринт который обходится не полностью) в виде 3-графа. Будах [5] построил шахматную ловушку для любого заданного конечного автомата. Отметим, что решение Будаха было довольно сложным (первые варианты содержали 175 страниц). Более наглядные решения данного вопроса представлены здесь [29, 31, 33, 34]. Антельман [2] оценил сложность подобной ловушки по числу клеток, а в [1] Антельман, Будах и Роллик сделали конечную ловушку для любой конечной системы автоматов. В постановке с шахматным лабиринтом и одним автоматом есть ещё ряд результатов, связанных с проблемами обходимости лабиринтов с различными числом дыр, с расслоениями лабиринтов по количеству состояний автомата и другими вопросами. Обзор подобных проблем можно найти например здесь [35]. Невозможность обхода всех плоских шахматных лабиринтов одним автоматом выдвинула вопрос об изучении возможных усилений модели автомата, которая решит задачу обхода. Основным способом усиления может являться рассмотрение коллектива автоматов,вместо одного автомата, взаимодействующих между собой. Частным и широко используемым случаем является рассмотрение системы из одного полноценного автомата и некоторого количества автоматов камней, которые не имеют внутреннего состояние и могут передвигаться только совместно с главным автоматом. Взаимодействие между автоматами является ключевой особенностью данного усиления, оно позволяется иметь коллективу (или одному автомату с камнями) внешнюю память, тем самым существенно разнообразит его поведение. Если от взаимодействия автоматов избавиться, то полученная независимая система будет немногим лучше одного автомата. Далее обсудим известные результаты связанные с коллективом автоматов

Exploring Topological Environments

Simultaneous localization and mapping (SLAM) addresses the task of incrementally building a map of the environment with a robot while simultaneously localizing the robot relative to that map. SLAM is generally regarded as one of the most important problems in the pursuit of building truly autonomous mobile robots. This thesis considers the SLAM problem within a topological framework, in which the world and its representation are modelled as a graph. A topological framework provides a useful model within which to explore fundamental limits to exploration and mapping. Given a topological world, it is not, in general, possible to map the world deterministically without resorting to some type of marking aids. Early work demonstrated that a single movable marker was sufficient but is this necessary? This thesis shows that deterministic mapping is possible if both explicit place and back-link information exist in one vertex. Such 'directional lighthouse' information can be established in a number of ways including through the addition of a simple directional immovable marker to the environment. This thesis also explores non-deterministic approaches that map the world with less marking information. The algorithms are evaluated through performance analysis and experimental validation. Furthermore, the basic sensing and locomotion assumptions that underlie these algorithms are evaluated using a differential drive robot and an autonomous visual sensor

Learning algorithms with applications to robot navigation and protein folding

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 109-117).by Mona Singh.Ph.D