Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

http://people.csail.mit.edu/lpk/papers/aij98-pomdp.pdf

Planning and Acting in Partially Observable Stochastic Domains

This beginning graduate textbook describes both recent achievements and classical results of computational complexity theory. Requiring essentially no background apart from mathematical maturity, the book can be used as a reference for self-study for anyone interested in complexity, including physicists, mathematicians, and other scientists, as well as a textbook for a variety of courses and seminars. More than 300 exercises are included with a selected hint set.

/pdf/computational-complexity-a-modern-approach-5gtk8urev9.pdf

Computational Complexity: A Modern Approach

In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area. @ 1997 Elsevier Science B.V.

/pdf/selection-of-relevant-features-and-examples-in-machine-5c8bbbwrgd.pdf

Selection of relevant features and examples in machine

Selection of relevant features and examples in machine learning

We introduce a new approach to planning in STRIPS-like domains based on constructing and analyzing a compact structure we call a Planning Graph. We describe a new planner, Graphplan, that uses this paradigm. Graphplan always returns a shortest-possible partial-order plan, or states that no valid plan exists.

We provide empirical evidence in favor of this approach, showing that Graphplan outperforms the total-order planner, Prodigy, and the partial-order planner, UCPOP, on a variety of interesting natural and artificial planning problems. We also give empirical evidence that the plans produced by Graphplan are quite sensible. Since searches made by this approach are fundamentally different from the searches of other common planning methods, they provide a new perspective on the planning problem.

/pdf/fast-planning-through-planning-graph-analysis-4jml8km3g5.pdf

Fast planning through planning graph analysis

https://www.cs.cmu.edu/~avrim/Papers/graphplan.pdf

A super-polynomial lower bound is given for the size of circuits of fixed depth computing the parity function. Introducing the notion of polynomial-size, constant-depth reduction, similar results are shown for the majority, multiplication, and transitive closure functions. Connections are given to the theory of programmable logic arrays and to the relativization of the polynomial-time hierarchy.

Parity, circuits and the polynomial time hierarchy

Parity, circuits, and the polynomial-time hierarchy

Modern cryptography has had considerable impact on the development of computational learning theory. Virtually every intractability result in Valiant’s model [13] (which is representation-independent in the sense that it does not rely on an artificial syntactic restriction on the learning algorithm’s hypotheses) has at its heart a cryptographic construction [4, 9, 1, 10]. In this paper, we give results in the reverse direction by showing how to construct several cryptographic primitives based on certain assumptions on the difficulty of learning. In doing so, we develop further a line of thought introduced by Impagliazzo and Levin [6].

/pdf/cryptographic-primitives-based-on-hard-learning-problems-47osv4m33l.pdf

Merrick L. Furst

Papers

Fast planning through planning graph analysis

Fast planning through planning graph analysis

Parity, circuits and the polynomial time hierarchy

Parity, circuits, and the polynomial-time hierarchy

Cryptographic Primitives Based on Hard Learning Problems