Study of Optimal Path Finding Techniques
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AI-KI-B Heuristic Search
AI-KI-B Heuristic Search Ute Schmid & Diedrich Wolter Practice: Johannes Rabold Cognitive Systems and Smart Environments Applied Computer Science, University of Bamberg last change: 3. Juni 2019, 10:05 Outline Introduction of heuristic search algorithms, based on foundations of problem spaces and search • Uniformed Systematic Search • Depth-First Search (DFS) • Breadth-First Search (BFS) • Complexity of Blocks-World • Cost-based Optimal Search • Uniform Cost Search • Cost Estimation (Heuristic Function) • Heuristic Search Algorithms • Hill Climbing (Depth-First, Greedy) • Branch and Bound Algorithms (BFS-based) • Best First Search • A* • Designing Heuristic Functions • Problem Types Cost and Cost Estimation • \Real" cost is known for each operator. • Accumulated cost g(n) for a leaf node n on a partially expanded path can be calculated. • For problems where each operator has the same cost or where no information about costs is available, all operator applications have equal cost values. For cost values of 1, accumulated costs g(n) are equal to path-length d. • Sometimes available: Heuristics for estimating the remaining costs to reach the final state. • h^(n): estimated costs to reach a goal state from node n • \bad" heuristics can misguide search! Cost and Cost Estimation cont. Evaluation Function: f^(n) = g(n) + h^(n) Cost and Cost Estimation cont. • \True costs" of an optimal path from an initial state s to a final state: f (s). • For a node n on this path, f can be decomposed in the already performed steps with cost g(n) and the yet to perform steps with true cost h(n). • h^(n) can be an estimation which is greater or smaller than the true costs. -
CAP 4630 Artificial Intelligence
CAP 4630 Artificial Intelligence Instructor: Sam Ganzfried [email protected] 1 • http://www.ultimateaiclass.com/ • https://moodle.cis.fiu.edu/ • HW1 out 9/5 today, due 9/28 (maybe 10/3) – Remember that you have up to 4 late days to use throughout the semester. – https://www.cs.cmu.edu/~sganzfri/HW1_AI.pdf – http://ai.berkeley.edu/search.html • Office hours 2 • https://www.cs.cmu.edu/~sganzfri/Calendar_AI.docx • Midterm exam: on 10/19 • Final exam pushed back (likely on 12/12 instead of 12/5) • Extra lecture on NLP on 11/16 • Will likely only cover 1-1.5 lectures on logic and on planning • Only 4 homework assignments instead of 5 3 Heuristic functions 4 8-puzzle • The object of the puzzle is to slide the tiles horizontally or vertically into the empty space until the configuration matches the goal configuration. • The average solution cost for a randomly generated 8-puzzle instance is about 22 steps. The branching factor is about 3 (when the empty tile is in the middle, four moves are possible; when it is in a corner, two; and when it is along an edge, three). This means that an exhaustive tree search to depth 22 would look at about 3^22 = 3.1*10^10 states. A graph search would cut this down by a factor of about 170,000 because only 181,440 distinct states are reachable (homework exercise). This is a manageable number, but for a 15-puzzle, it would be 10^13, so we will need a good heuristic function. -
Quantum Algorithms for Matching Problems
Quantum Algorithms for Matching Problems Sebastian D¨orn Institut f¨urTheoretische Informatik, Universit¨atUlm, 89069 Ulm, Germany [email protected] Abstract. We present quantum algorithms for the following matching problems in unweighted and weighted graphs with n vertices and m edges: √ – Finding a maximal matching in general graphs in time O( nm log2 n). √ – Finding a maximum matching in general graphs in time O(n m log2 n). – Finding a maximum weight matching in bipartite graphs in time √ O(n mN log2 n), where N is the largest edge weight. – Finding a minimum weight perfect matching in bipartite graphs in √ time O(n nm log3 n). These results improve the best classical complexity bounds for the corre- sponding problems. In particular, the second result solves an open ques- tion stated in a paper by Ambainis and Spalekˇ [AS06]. 1 Introduction A matching in a graph is a set of edges such that for every vertex at most one edge of the matching is incident on the vertex. The task is to find a matching of maximum cardinality. The matching problem has many important applications in graph theory and computer science. In this paper we study the complexity of algorithms for the matching prob- lems on quantum computers and compare these to the best known classical algo- rithms. We will consider different versions of the matching problems, depending on whether the graph is bipartite or not and whether the graph is unweighted or weighted. For unweighted graphs, the best classical algorithm for finding a match- ing of maximum√ cardinality is based on augmenting paths and has running time O( nm) (see Micali and Vazirani [MV80]). -
Fast and Efficient Black Box Optimization Using the Parameter-Less Population Pyramid
Fast and Efficient Black Box Optimization Using the Parameter-less Population Pyramid B. W. Goldman [email protected] Department of Computer Science and Engineering, Michigan State University, East Lansing, 48823, USA W. F. Punch [email protected] Department of Computer Science and Engineering, Michigan State University, East Lansing, 48823, USA doi:10.1162/EVCO_a_00148 Abstract The parameter-less population pyramid (P3) is a recently introduced method for per- forming evolutionary optimization without requiring any user-specified parameters. P3’s primary innovation is to replace the generational model with a pyramid of mul- tiple populations that are iteratively created and expanded. In combination with local search and advanced crossover, P3 scales to problem difficulty, exploiting previously learned information before adding more diversity. Across seven problems, each tested using on average 18 problem sizes, P3 outperformed all five advanced comparison algorithms. This improvement includes requiring fewer evaluations to find the global optimum and better fitness when using the same number of evaluations. Using both algorithm analysis and comparison, we find P3’s effectiveness is due to its ability to properly maintain, add, and exploit diversity. Unlike the best comparison algorithms, P3 was able to achieve this quality without any problem-specific tuning. Thus, unlike previous parameter-less methods, P3 does not sacrifice quality for applicability. There- fore we conclude that P3 is an efficient, general, parameter-less approach to black box optimization which is more effective than existing state-of-the-art techniques. Keywords Genetic algorithms, linkage learning, local search, parameter-less. 1 Introduction A primary purpose of evolutionary optimization is to efficiently find good solutions to challenging real-world problems with minimal prior knowledge about the problem itself. -
Lecture 18 Solving Shortest Path Problem: Dijkstra's Algorithm
Lecture 18 Solving Shortest Path Problem: Dijkstra’s Algorithm October 23, 2009 Lecture 18 Outline • Focus on Dijkstra’s Algorithm • Importance: Where it has been used? • Algorithm’s general description • Algorithm steps in detail • Example Operations Research Methods 1 Lecture 18 One-To-All Shortest Path Problem We are given a weighted network (V, E, C) with node set V , edge set E, and the weight set C specifying weights cij for the edges (i, j) ∈ E. We are also given a starting node s ∈ V . The one-to-all shortest path problem is the problem of determining the shortest path from node s to all the other nodes in the network. The weights on the links are also referred as costs. Operations Research Methods 2 Lecture 18 Algorithms Solving the Problem • Dijkstra’s algorithm • Solves only the problems with nonnegative costs, i.e., cij ≥ 0 for all (i, j) ∈ E • Bellman-Ford algorithm • Applicable to problems with arbitrary costs • Floyd-Warshall algorithm • Applicable to problems with arbitrary costs • Solves a more general all-to-all shortest path problem Floyd-Warshall and Bellman-Ford algorithm solve the problems on graphs that do not have a cycle with negative cost. Operations Research Methods 3 Lecture 18 Importance of Dijkstra’s algorithm Many more problems than you might at first think can be cast as shortest path problems, making Dijkstra’s algorithm a powerful and general tool. For example: • Dijkstra’s algorithm is applied to automatically find directions between physical locations, such as driving directions on websites like Mapquest or Google Maps. -
Faster Shortest-Path Algorithms for Planar Graphs
Journal of Computer and System SciencesSS1493 journal of computer and system sciences 55, 323 (1997) article no. SS971493 Faster Shortest-Path Algorithms for Planar Graphs Monika R. Henzinger* Department of Computer Science, Cornell University, Ithaca, New York 14853 Philip Klein- Department of Computer Science, Brown Univrsity, Providence, Rhode Island 02912 Satish Rao NEC Research Institute and Sairam Subramanian Bell Northern Research Received April 18, 1995; revised August 13, 1996 and matching). In this paper we improved algorithms for We give a linear-time algorithm for single-source shortest paths in single-source shortest paths in planar networks. planar graphs with nonnegative edge-lengths. Our algorithm also The first algorithm handles the important special case of yields a linear-time algorithm for maximum flow in a planar graph nonnegative edge-lengths. For this case, we obtain a linear- with the source and sink on the same face. For the case where time algorithm. Thus our algorithm is optimal, up to con- negative edge-lengths are allowed, we give an algorithm requiring O(n4Â3 log(nL)) time, where L is the absolute value of the most stant factors. No linear-time algorithm for shortest paths in negative length. This algorithm can be used to obtain similar bounds for planar graphs was previously known. For general graphs computing a feasible flow in a planar network, for finding a perfect the best bounds known in the standard model, which for- matching in a planar bipartite graph, and for finding a maximum flow in bids bit-manipulation of the lengths, is O(m+n log n) time, a planar graph when the source and sink are not on the same face. -
Artificial Intelligence 1: Informed Search
Informed Search II 1. When A* fails – Hill climbing, simulated annealing 2. Genetic algorithms When A* doesn’t work AIMA 4.1 A few slides adapted from CS 471, UBMC and Eric Eaton (in turn, adapted from slides by Charles R. Dyer, University of Wisconsin-Madison)...... Outline Local Search: Hill Climbing Escaping Local Maxima: Simulated Annealing Genetic Algorithms CIS 391 - Intro to AI 3 Local search and optimization Local search: • Use single current state and move to neighboring states. Idea: start with an initial guess at a solution and incrementally improve it until it is one Advantages: • Use very little memory • Find often reasonable solutions in large or infinite state spaces. Useful for pure optimization problems. • Find or approximate best state according to some objective function • Optimal if the space to be searched is convex CIS 391 - Intro to AI 4 Hill Climbing Hill climbing on a surface of states h(s): Estimate of distance from a peak (smaller is better) OR: Height Defined by Evaluation Function (greater is better) CIS 391 - Intro to AI 6 Hill-climbing search I. While ( uphill points): • Move in the direction of increasing evaluation function f II. Let snext = arg maxfs ( ) , s a successor state to the current state n s • If f(n) < f(s) then move to s • Otherwise halt at n Properties: • Terminates when a peak is reached. • Does not look ahead of the immediate neighbors of the current state. • Chooses randomly among the set of best successors, if there is more than one. • Doesn’t backtrack, since it doesn’t remember where it’s been a.k.a. -
A. Local Beam Search with K=1. A. Local Beam Search with K = 1 Is Hill-Climbing Search
4. Give the name that results from each of the following special cases: a. Local beam search with k=1. a. Local beam search with k = 1 is hill-climbing search. b. Local beam search with one initial state and no limit on the number of states retained. b. Local beam search with k = ∞: strictly speaking, this doesn’t make sense. The idea is that if every successor is retained (because k is unbounded), then the search resembles breadth-first search in that it adds one complete layer of nodes before adding the next layer. Starting from one state, the algorithm would be essentially identical to breadth-first search except that each layer is generated all at once. c. Simulated annealing with T=0 at all times (and omitting the termination test). c. Simulated annealing with T = 0 at all times: ignoring the fact that the termination step would be triggered immediately, the search would be identical to first-choice hill climbing because every downward successor would be rejected with probability 1. d. Simulated annealing with T=infinity at all times. d. Simulated annealing with T = infinity at all times: ignoring the fact that the termination step would never be triggered, the search would be identical to a random walk because every successor would be accepted with probability 1. Note that, in this case, a random walk is approximately equivalent to depth-first search. e. Genetic algorithm with population size N=1. e. Genetic algorithm with population size N = 1: if the population size is 1, then the two selected parents will be the same individual; crossover yields an exact copy of the individual; then there is a small chance of mutation. -
On the Design of (Meta)Heuristics
On the design of (meta)heuristics Tony Wauters CODeS Research group, KU Leuven, Belgium There are two types of people • Those who use heuristics • And those who don’t ☺ 2 Tony Wauters, Department of Computer Science, CODeS research group Heuristics Origin: Ancient Greek: εὑρίσκω, "find" or "discover" Oxford Dictionary: Proceeding to a solution by trial and error or by rules that are only loosely defined. Tony Wauters, Department of Computer Science, CODeS research group 3 Properties of heuristics + Fast* + Scalable* + High quality solutions* + Solve any type of problem (complex constraints, non-linear, …) - Problem specific (but usually transferable to other problems) - Cannot guarantee optimallity * if well designed 4 Tony Wauters, Department of Computer Science, CODeS research group Heuristic types • Constructive heuristics • Metaheuristics • Hybrid heuristics • Matheuristics: hybrid of Metaheuristics and Mathematical Programming • Other hybrids: Machine Learning, Constraint Programming,… • Hyperheuristics 5 Tony Wauters, Department of Computer Science, CODeS research group Constructive heuristics • Incrementally construct a solution from scratch. • Easy to understand and implement • Usually very fast • Reasonably good solutions 6 Tony Wauters, Department of Computer Science, CODeS research group Metaheuristics A Metaheuristic is a high-level problem independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Instead of reinventing the wheel when developing a heuristic -
Shortest Path Problems and Algorithms
Shortest path problems and algorithms 1. Shortest path problems . Shortest path problem . Single-source shortest path problem . Single-destination shortest path problem . All-pairs shortest path problem 2. Algorithms . Dijkstra’s algorithm for single source shortest path problem (positive weight) . Bellman–Ford algorithm for single source shortest path problem (allow negative weight) . Floyd–Warshall algorithm for all pairs shortest paths . Johnson's algorithm for all pairs shortest paths CP412 ADAII 1. Shortest path problems Shortest path problem: Input: weight graph G = (V, E, W), source vertex s and destination vertex t. Output: a shortest path of G from s to t. Single-source shortest path problem: Output: shortest paths from source vertex s to all other vertices of G. Single-destination shortest path problem: Output: shortest paths from all vertices of G to a single destination t. All-pairs shortest path problem: Output: shortest paths between every pair of vertices u, v in the graph. CP412 ADAII History of SPP and algorithms Shimbel (1955). Information networks. Ford (1956). RAND, economics of transportation. Leyzorek, Gray, Johnson, Ladew, Meaker, Petry, Seitz (1957). Combat Development Dept. of the Army Electronic Proving Ground. Dantzig (1958). Simplex method for linear programming. Bellman (1958). Dynamic programming. Moore (1959). Routing long-distance telephone calls for Bell Labs. Dijkstra (1959). Simpler and faster version of Ford's algorithm. CP412 ADAII Dijkstra’s algorithms Edger W. Dijkstra’s quotes . The question of whether computers can think is like the question of whether submarines can swim. In their capacity as a tool, computers will be but a ripple on the surface of our culture. -
The On-Line Shortest Path Problem Under Partial Monitoring
Journal of Machine Learning Research 8 (2007) 2369-2403 Submitted 4/07; Revised 8/07; Published 10/07 The On-Line Shortest Path Problem Under Partial Monitoring Andras´ Gyor¨ gy [email protected] Machine Learning Research Group Computer and Automation Research Institute Hungarian Academy of Sciences Kende u. 13-17, Budapest, Hungary, H-1111 Tamas´ Linder [email protected] Department of Mathematics and Statistics Queen’s University, Kingston, Ontario Canada K7L 3N6 Gabor´ Lugosi [email protected] ICREA and Department of Economics Universitat Pompeu Fabra Ramon Trias Fargas 25-27 08005 Barcelona, Spain Gyor¨ gy Ottucsak´ [email protected] Department of Computer Science and Information Theory Budapest University of Technology and Economics Magyar Tudosok´ Kor¨ utja´ 2. Budapest, Hungary, H-1117 Editor: Leslie Pack Kaelbling Abstract The on-line shortest path problem is considered under various models of partial monitoring. Given a weighted directed acyclic graph whose edge weights can change in an arbitrary (adversarial) way, a decision maker has to choose in each round of a game a path between two distinguished vertices such that the loss of the chosen path (defined as the sum of the weights of its composing edges) be as small as possible. In a setting generalizing the multi-armed bandit problem, after choosing a path, the decision maker learns only the weights of those edges that belong to the chosen path. For this problem, an algorithm is given whose average cumulative loss in n rounds exceeds that of the best path, matched off-line to the entire sequence of the edge weights, by a quantity that is proportional to 1=pn and depends only polynomially on the number of edges of the graph. -
Econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Nikulin, Yury Working Paper — Digitized Version Solving the robust shortest path problem with interval data using a probabilistic metaheuristic approach Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel, No. 597 Provided in Cooperation with: Christian-Albrechts-University of Kiel, Institute of Business Administration Suggested Citation: Nikulin, Yury (2005) : Solving the robust shortest path problem with interval data using a probabilistic metaheuristic approach, Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel, No. 597, Universität Kiel, Institut für Betriebswirtschaftslehre, Kiel This Version is available at: http://hdl.handle.net/10419/147655 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence. www.econstor.eu Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel No.