<u>Correction</u> to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths"

  title={<u>Correction</u> to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths"},
  author={P. Hart and N. Nilsson and B. Raphael},
  journal={SIGART Newsl.},
Our paper on the use of heuristic information in graph searching defined a path-finding algorithm, A*, and proved that it had two important properties. In the notation of the paper, we proved that if the heuristic function ñ (n) is a lower bound on the true minimal cost from node n to a goal node, then A* is <u>admissible;</u> i.e., it would find a minimal cost path if any path to a goal node existed. Further, we proved that if the heuristic function also satisfied something called the <u… Expand
On the Complexity of Admissible Search Algorithms
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This paper analyzes the complexity of heuristic search algorithms, i.e. algorithms which find the shortest path in a graph by using an estimate to guide the search and presents a new search algorithm which runs in O(N2) steps in the worst case and which never requires more steps than A*. Expand
Completeness and Admissibility for General Heuristic Search Algorithms—A Theoretical Study: Basic Concepts and Proofs
General theorems about the completeness and the sub-admissibility that widely extend the previous results are proved and provide a theoretical support for using diverse kinds of Heuristic Search algorithms in enlarged contexts, specially when the state graphs and the evaluation functions are less constrained than ordinarily. Expand
Generalized best-first search strategies and the optimality of A*
It is shown that several known properties of A* retain their form and it is also shown that no optimal algorithm exists, but if the performance tests are confirmed to cases in which the estimates are also consistent, then A* is indeed optimal. Expand
A heuristic search approach for solving a minimum path problem requiring arc cost determination
Two generalizations of A*, BA* and DA, which consider the problem of finding a minimum cost path from the start node to a finite goal node set in a directed OR-graph, are presented and analyzed. Expand
A Metric Space Approach to the Specification of the Heuristic Function for the A* Algorithm
It is shown how to specify an admissible and monotone heuristic function for a wide class of problem domains and applications to an optimal parts distribution problem in flexible manufacturing systems and artificial intelligence planning problems are provided. Expand
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An improved proof is presented for a theorem on search algorithms which find minimal cost paths in a graph. The theorem essentially states that when searching for a minimal cost path in a graph, aExpand
A heuristic search algorithm for path determination with learning
An algorithm for finding a least-cost-path from start node to goal node set in a directed graph, adaptive A*(AA*), which can be used to automate knowledge acquisition, so that A* exhibits a form of machine learning. Expand
A best-first search algorithm guided by a set-valued heuristic
Presents an algorithm, called A/sup G/, for finding the least-cost path from start node to goal node set in an OR-graph, where arc costs are scalar-valued and the cost of each path is the sum of theExpand
Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties
This paper focuses on a variant of this problem in which additional penalties are incurred at the vertices, and proposes two variants of Dijkstra’s algorithm that operate on the original, unexpanded graph. Expand
Multiobjective A*
A multiobjective generalization of the heuristic search algorithm A* is presented and it is shown that &fOA * is complete and, when used with a suitably defined set of admissible heuristic functions, admissible. Expand


Review of "Problem-Solving Methods in Artificial Intelligence by Nils J. Nilsson", McGraw-Hill Pub.
This book is not a survey on theorem proving programs, but the description of a program developed from 1960 to 1965, and includes three chapters that deal with resolution-based theorem-proving in the predicate calculus and its applications to problem solving. Expand
Realization of a geometry theorem proving machine
The technique of heuristic programming is under detailed investigation as a means to the end of applying large-scale digital computers to the solution of a difficult class of problems currently considered to be beyond their capabilities; namely those problems that seem to require the agent of human intelligence and ingenuity for their solution. Expand
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Enough work has been done by verify the fact that a computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program. Expand
Computers and Thought
Computers and Thought showcases the work of the scientists who not only defined the field of Artificial Intelligence, but who are responsible for having developed it into what it is today. OriginallyExpand
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Write to: Miss Liz Klein ACM 1133 Avenue of the Americas New York
  • Write to: Miss Liz Klein ACM 1133 Avenue of the Americas New York