Reactive planning

Abstract: Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective. Table of Contents: Preface / Planning and Autonomous Behavior / Classical Planning: Full Information and Deterministic Actions / Classical Planning: Variations and Extensions / Beyond Classical Planning: Transformations / Planning with Sensing: Logical Models / MDP Planning: Stochastic Actions and Full Feedback / POMDP Planning: Stochastic Actions and Partial Feedback / Discussion / Bibliography / Author's Biography

Abstract: Tanagra is a mixed-initiative tool for level design, allowing a human and a computer to work together to produce a level for a 2-D platformer. An underlying, reactive level generator ensures that all levels created in the environment are playable, and provides the ability for a human designer to rapidly view many different levels that meet their specifications. The human designer can iteratively refine the level by placing and moving level geometry, as well as through directly manipulating the pacing of the level. This paper presents the design environment, its underlying architecture that integrates reactive planning and numerical constraint solving, and an evaluation of Tanagra's expressive range.

Abstract: We present TangentBug, a new range-sensor based navigation algorithm for two degrees-of-freedom mobile robots. The algorithm combines local reactive planning with globally convergent behaviour. For the local planning, TangentBug uses the range data to compute a locally shortest path based on a novel structure, termed the local tangent graph (LTG). The robot uses the LTG for choosing the locally optimal direction while moving towards the target. The robot also uses the LTG in its other motion mode, where it follows an obstacle boundary. In this mode the robot uses the LTG for making local short-cuts and testing a leaving condition which allows the robot to resume its motion towards the target. We analyze the convergence and performance properties of TangentBug. We also present simulation results, showing that TangentBug consistently performs better than the classical VisBug algorithm. Moreover, TangentBug produces paths that in simple environments approach the globally optimal path as the sensor's maximal detection range increases.