'Search graph' presentation slideshows

CS B551 : Elements of Artificial Intelligence

CS B551 : Elements of Artificial Intelligence. Instructor: Kris Hauser http://cs.indiana.edu/~hauserk. Agenda. Searching, data structures, and algorithms Breadth-first search Depth-first search Uniform-cost search. Defining a Search Problem. S. State space S

759 views • 63 slides

CS B351 : Intro to AI and Computer Simulation

CS B351 : Intro to AI and Computer Simulation. Instructor: Kris Hauser http://cs.indiana.edu/~hauserk. Agenda. Searching, data structures, and algorithms Breadth-first search Depth-first search Uniform-cost search. Defining a Search Problem. S. State space S

722 views • 63 slides

CS 343H: Artificial Intelligence

CS 343H: Artificial Intelligence. Week2a: Uninformed Search. Today. Agents that Plan Ahead Search Problems Uninformed Search Methods Depth-First Search Breadth-First Search Uniform-Cost Search. Agent. Sensors. Percepts. ?. Environment. Actuators. Actions. Recall: Rational Agents.

546 views • 54 slides

Learning from Learning Curves: Item Response Theory & Learning Factors Analysis

Learning from Learning Curves: Item Response Theory & Learning Factors Analysis . Ken Koedinger Human-Computer Interaction Institute Carnegie Mellon University.

662 views • 42 slides

Adaptive Graph Pattern Ma t ching for Model Transformations using Model -sensitive Search Plans

500 views • 40 slides

Artificial Intelligence Chapter 9 Heuristic Search

Artificial Intelligence Chapter 9 Heuristic Search. Outline. Using Evaluation Functions A General Graph-Searching Algorithm Algorithm A* Iterative-Deepening A* Heuristic Functions and Search Efficiency. 9.1 Using Evaluation Functions. Best-first search (BFS) = Heuristic search

354 views • 35 slides

Uninformed Search Strategies

418 views • 35 slides

Artificial Intelligence Chapter 9 Heuristic Search

Artificial Intelligence Chapter 9 Heuristic Search. Biointelligence Lab School of Computer Sci. & Eng. Seoul National University. Outline. Using Evaluation Functions A General Graph-Searching Algorithm Algorithm A* Iterative-Deepening A* Heuristic Functions and Search Efficiency.

358 views • 35 slides

316 views • 22 slides

ACTION RULES /Lecture II/

ACTION RULES /Lecture II/. presented by Zbigniew Ras UNC-Charlotte, Computer Science. Action Rules [Z. Ras & A. Wieczorkowska]. Decision table. Any information system of the form S = (U, A Fl ? A St ? {d}), where d ? A Fl ? A St is a distinguished attribute called decision.

357 views • 22 slides

Informed Search and Exploration

Informed Search and Exploration. Search Strategies Heuristic Functions Local Search Algorithms. Introduction. Heuristic search strategies: Use problem specific knowledge Can find solutions more efficiently than search strategies that do not use domain specific knowledge.

339 views • 11 slides

Constraint-based problem solving

Constraint-based problem solving. Model problem specify in terms of constraints on acceptable solutions define variables (denotations) and domains define constraints in some language Solve model define search space / choose algorithm incremental assignment / backtracking search

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First Social Networking Platform with semantic search comple

Zahdoo.com is the next generation Social Utility Platform offering users a private cognitive Social Network to connect families and their social circles and help them interact through conversations to experience connectedness while being apart.

182 views • 4 slides

CS 260 Search Exercise

CS 260 Search Exercise. (IDDFS: A | A B C D | A B E F C G H D I J K | A B E F L ). Consider the search graph below. The h value of a node is given adjacent to that node. The actual cost of traversing

208 views • 1 slides