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Greedy mapping of terrain
"... We study a greedy mapping method that always moves the robot from its current location to the closest location that it has not visited (or observed) yet, until the terrain is mapped. Although one does not expect such a simple mapping method to minimize the travel distance of the robot, we present an ..."
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We study a greedy mapping method that always moves the robot from its current location to the closest location that it has not visited (or observed) yet, until the terrain is mapped. Although one does not expect such a simple mapping method to minimize the travel distance of the robot, we present analytical results that show (perhaps surprisingly) that the travel distance of the robot is reasonably small. This is interesting because greedy mapping has a number of desirable properties. It is simple to implement and integrate into complete robot architectures. It does not need to have control of the robot at all times, takes advantage of prior knowledge about parts of the terrain (if available), and can be used by several robots cooperatively.
Exploring an unknown graph efficiently
 In Proc. 13th Annu. European Sympos. Algorithms
, 2005
"... Abstract. We study the problem of exploring an unknown, strongly connected directed graph. Starting at some node of the graph, we must visit every edge and every node at least once. The goal is to minimize the number of edge traversals. It is known that the competitive ratio of online algorithms for ..."
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Abstract. We study the problem of exploring an unknown, strongly connected directed graph. Starting at some node of the graph, we must visit every edge and every node at least once. The goal is to minimize the number of edge traversals. It is known that the competitive ratio of online algorithms for this problem depends on the deficiency d of the graph, which is the minimum number of edges that must be added to make the graph Eulerian. We present the first deterministic online exploration algorithm whose competitive ratio is polynomial in d (it is O(d 8)). 1
Online Graph Exploration: New Results on Old and New Algorithms
"... We study the problem of exploring an unknown undirected connected graph. Beginning in some start vertex, a searcher must visit each node of the graph by traversing edges. Upon visiting a vertex for the first time, the searcher learns all incident edges and their respective traversal costs. The goal ..."
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We study the problem of exploring an unknown undirected connected graph. Beginning in some start vertex, a searcher must visit each node of the graph by traversing edges. Upon visiting a vertex for the first time, the searcher learns all incident edges and their respective traversal costs. The goal is to find a tour of minimum total cost. Kalyanasundaram and Pruhs [23] proposed a sophisticated generalization of a Depth First Search that is 16competitive on planar graphs. While the algorithm is feasible on arbitrary graphs, the question whether it has constant competitive ratio in general has remained open. Our main result is an involved lower bound construction that answers this question negatively. On the positive side, we prove that the algorithm has constant competitive ratio on any class of graphs with bounded genus. Furthermore, we provide a constant competitive algorithm for general graphs with a bounded number of distinct weights.
UTBot: A Virtual Agent Platform for Teaching Agent System Design
"... Abstract—We introduce UTBot, a virtual agent platform for teaching agent system design. UTBot implements a client for the Unreal Tournament game server and Gamebots system. It provides students with the basic functionality required to start developing their own intelligent virtual agents to play aut ..."
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Abstract—We introduce UTBot, a virtual agent platform for teaching agent system design. UTBot implements a client for the Unreal Tournament game server and Gamebots system. It provides students with the basic functionality required to start developing their own intelligent virtual agents to play autonomously UT games. UTBot includes a generic agent architecture, CAA (Contextsensitive Agent Architecture), a domainspecific world model, a visualization tool, several basic strategies (represented by internal modes and internal behaviors), and skills (represented by external behaviors). The CAA architecture can support complex longterm behaviors as well as reactive shortterm behaviors. It also realizes high contextsensitivity of behaviors. We also discuss our experience using UTBot as a pedagogical tool for teaching agent system design in undergraduate Artificial Intelligence course. Index Terms—interactive computer game, agent system design, agent architecture, virtual agent, pedagogical tool, artificial intelligence I.
Easy Stabilization with an Agent
"... The paper presents a technique for achieving stabilization in distributed systems. This technique, called agentstabilization, uses an external tool, the agent, that can be considered as a special message created by a lower layer. Basically, an agent performs a traversal of the network and if ne ..."
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The paper presents a technique for achieving stabilization in distributed systems. This technique, called agentstabilization, uses an external tool, the agent, that can be considered as a special message created by a lower layer. Basically, an agent performs a traversal of the network and if necessary, modi es the local states of the nodes, yielding stabilization.
Lifelong Planning for Mobile Robots
"... Mobile robots often have to replan as their knowledge of the world changes. Lifelong planning is a paradigm that allows them to replan much faster than with complete searches from scratch, yet finds optimal solutions. To demonstrate this paradigm, we apply it to Greedy Mapping, a simple sensorbased ..."
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Mobile robots often have to replan as their knowledge of the world changes. Lifelong planning is a paradigm that allows them to replan much faster than with complete searches from scratch, yet finds optimal solutions. To demonstrate this paradigm, we apply it to Greedy Mapping, a simple sensorbased planning method that always moves the robot from its current cell to the closest cell that it has not observed yet, until the terrain is mapped. Greedy Mapping has a small mapping time, makes only action recommendations and can thus coexist with other components of a robot architecture that also make action recommendations, and is able to take advantage of prior knowledge of parts of the terrain (if available). We demonstrate how a robot can use our lifelongplanning version of A* to repeatedly determine a shortest path from its current cell to the closest cell that it has not observed yet. Our experimental results demonstrate the advantage of lifelong planning for Greedy Mapping over other search methods. Similar results had so far been established only for goaldirected navigation in unknown terrain. 1
Graduate Group Chairperson COPYRIGHT
, 2006
"... ii iii To my parents, my sister and my fiancé ACKNOWLEDGMENTS This dissertation has benefited from the comments and suggestions of many people and thus I would like to take this opportunity to express my gratitude. Firstly, I would like to thank my advisor, Professor Norman Badler, for his helpful g ..."
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ii iii To my parents, my sister and my fiancé ACKNOWLEDGMENTS This dissertation has benefited from the comments and suggestions of many people and thus I would like to take this opportunity to express my gratitude. Firstly, I would like to thank my advisor, Professor Norman Badler, for his helpful guidance and support over the past three years. I feel very fortunate to have had the experience of doing research with him. While conducting this research, I had the opportunity to work with and learn from many people. I am especially grateful to Jan Allbeck for her thoughtful advice and encouragement. I am also thankful to all the students at the Center for Human Modeling and Simulation for their feedback and advice during our reading group sessions. I would also like to thank Professor Ali Malkawi for giving me the opportunity to participate in some evacuation simulation projects for the Building and
Graph Exploration
"... acks on the just visited edge and is then lead to the third node. In any case, the triangle edges must be traversed twice to reach all the nodes again (we still must explore the selfloops). Note that our graph G is Eulerian (because each node has even degree), i.e., there exists a tour in G visiti ..."
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acks on the just visited edge and is then lead to the third node. In any case, the triangle edges must be traversed twice to reach all the nodes again (we still must explore the selfloops). Note that our graph G is Eulerian (because each node has even degree), i.e., there exists a tour in G visiting each edge exactly once. Since the selfloops only have to be traversed once, the algorithm would only traverse the triangle edges twice, so the lower bound for the competitive ratio on this graph would be 3 2 . But we can make the triangle edges arbitrarily heavy (by replacing them with a long path), and then the competitive ratio approaches 2. DFS has the property that it visits every edge of an undirected graph exactly twice, so it is clearly 2competitive. ut 1 Kwek [6] investigated what happens if we run DFS on a directed graph. Before we state his results, we need a few notations (taken