- Case Based Heuristic Selection for Exam Timetabling Problems
- Hyper-heuristics Course Introduction 1 Summer School Course, Istanbul Technical University, 31st Jul 3rd Aug 2007
- Artificial Intelligence Methods rxq@cs.nott.ac.uk
- Introduction The Formulation The Hybrid Approach Computational Experiment Results Conclusions A Hybrid Constraint Programming
- Planning in probabilistic domains using a deterministic numeric planner Sergio Jimenez
- Tools, and Examples Expert Systems
- N. R. Sabar M. Ayob Data Mining and Optimisation Research Group (DMO), Centre for Artificial Intelligent
- Constraint Logic Programming Constraint Propagation -Consistency
- Program Chairs: Edmund Burke, Graham Kendall, Stephen Smith, and Kay Chen Tan
- Introduction to Artificial Intelligence Problem Space and Search Tree
- Unary resources Cumulative resources Alternative resources Summary Constraint-based Scheduling: Propagation
- Constraint Logic Programming Introduction to Scheduling
- Solving Multi-objective Multicast Routing Problems by Evolutionary Multi-objective Simulated Annealing Algorithms
- A polynomial algorithm for a bi-criteria cyclic scheduling problem Olivier Marchetti, Alix Munier-Kordon
- An empirical study of planning and scheduling interactions in the road passenger transportation domain
- September 2002 1 Case Based Reasoning as a Heuristic Selector
- Adaptive Selection of Heuristics within a GRASP for Exam Timetabling Problems
- 2009 IEEE Symposium on Computational Intelligence in Scheduling (CI-Sched 2009)
- IEEE Transactions on Evolutionary Computation Special Issue on Evolutionary Computation in Scheduling
- A Hybrid Model of Integer Programming and Variable Neighbourhood Search for Highly-Constrained Nurse Rostering Problems
- Introduction to Artificial Intelligence Discuss what is meant by Artificial Intelligence (AI)
- Fuzzy Greedy Search and Job-Shop Problem Kaveh Sheibani
- Airport flight gate scheduling with constraint programming Flight gate scheduling is concerned with finding an assignment of flights to terminal or ramp positions,
- Constraint Logic Programming Constraint Optimisation Problems
- Analyzing the Landscape of a Graph Based Hyper-heuristic for Timetabling Problems
- HIERARCHICAL METHOD FOR NURSE ROSTERING BASED ON GRANULAR PRE-PROCESSING OF CONSTRAINTS
- Constraint Logic Programming Modeling CSPs Case Study II
- SSCI 2007 -Computational Intelligence in Paradise! 2007 IEEE Symposium Series on Computational Intelligence (SSCI 2007)
- SPECIFICATION CASE STUDIES Second Edition
- Constraint Logic Programming G53CLP Module Introduction
- A Survey of Search Methodologies and Automated System Development for
- Decision Support Methodologies E-mail: rxq@cs.nott.ac.uk
- Artificial Intelligence Methods rxq@cs.nott.ac.uk
- Theory and Practice of Constraint Propagation Roman Bartk*
- PLEASE SCROLL DOWN FOR ARTICLE This article was downloaded by: [University of Nottingham]
- An Investigation of a Tabu Assisted Hyper-Heuristic Genetic Limin Han, Graham Kendall
- Artificial Intelligence Methods rxq@cs.nott.ac.uk
- Dr. Rong Qu rxqdsFnottFFuk
- Submitted to: c Fang He & Rong Qu
- Model Transfer for Markov Decision Tasks via Parameter Matching Funlade T. Sunmola
- Introduction to Artificial Intelligence Knowledge Representation &
- Some sample questions are given for each session of the module. The exam questions cover all content in the lectures.
- University of Leeds SCHOOL OF COMPUTING
- Introduction to Z Dr. Rong Qu
- Constraint Logic Programming Introduction to CP Techniques &
- Foundations of Artificial Intelligence Knowledge Representation
- Expert System Applications Expert Systems
- Introduction to Artificial Intelligence Neural Networks
- November 2002 1 Case Based Heuristic Selection for Examination
- 1Nur Evin zdemirel -IE 505 Heuristic Search Variable Neighborhood Search
- Decision Support Methodologies E-mail: rxq@cs.nott.ac.uk
- School of Computer Science and Information Technology University of Nottingham
- G5AIAI Introduction to AI Exam The rubric for this examination should read as follows
- Formal Specification: a Roadmap Axel van Lamsweerde
- Learning Macro-Actions Genetically from Plans M.A. Hakim Newton, John Levine, Maria Fox, Derek Long
- Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies, Volume 82. Proceedings of KES'02, 336-340. Sep, 2002
- Investigating a Graph-Based Hyper-heuristic for Timetabling Problems
- Adaptive Automated Construction of Hybrid Heuristics for Exam Timetabling and Graph Colouring Problems
- Pareto-Based Optimization for Multi-objective Nurse Scheduling Edmund K. Burke, Jingpeng Li* and Rong Qu
- School of Computer Science and Information Technology University of Nottingham
- School of Computer Science and Information Technology University of Nottingham
- JOSH Editorial Rong Qu, Maria Fox and Derek Long
- A Population Based Incremental Learning for Delay Constrained Network Coding Resource Minimization
- A Variable Neighborhood Descent Search Algorithm for Delay-Constrained Least-Cost
- Roulette wheel Graph Colouring for Solving Examination Timetabling Problems
- CASE-BASED REASONING FOR COURSE TIMETABLING PROBLEMS
- Recent Research on Nurse Rostering and Others Recent Developments on Nurse
- Recent Research on Nurse Rostering in ASAP Recent Research on Nurse
- Analyzing heuristic hybridizations using a hyper-heuristic for timetabling problems
- A Hybrid VNS within a Graph Based Hyper-heuristic for Timetabling Problems
- An investigation on a graph based hyper-heuristic for timetabling problems
- A DECOMPOSITION, CONSTRUCTION AND POSTPROCESSING APPROACH
- 1PATAT'04,Pittsburgh Analysing Similarity in Examination Timetabling
- Case Based Heuristic Selection for Exam Timetabling Problems
- Symposium Series on Computational Intelligence IEEE SSCI 2011 April 11 15, 2011 Paris, France
- Using Learned Action Models in Execution Monitoring Maria Fox, Jonathan Gough and Derek Long
- Fast Trajectory Planning for Multiple Site Surveillance through Moving Obstacles and Wind
- Multi-period Production Setup-sequencing and Lot-sizing through ATSP Subtour Elimination and Patching
- Methods for Optimal Pedestrian Task Scheduling and Routing Srihari Narasimhan
- Modelling Alternatives in Temporal Networks Roman Bartk*, Ondej Cepek* , Pavel Surynek*
- Generating Dynamic Activity-Travel Schedules Marlies Vanhulsel, Davy Janssens, Geert Wets
- Introduction to Artificial Intelligence (G51IAI)
- Introduction to Artificial Intelligence History of AI
- Introduction to Artificial Intelligence Blind Searches -Introduction
- Introduction to Artificial Intelligence An Introduction to Data Mining
- The University of Nottingham SCHOOL OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY
- Scheduling CSP model Resources Optimization Basic Class Timetabling Constraint-based Scheduling: Introduction
- Constraint Logic Programming Constraint Propagation -
- Constraint Logic Programming Issues in Modeling CSPs
- G53CLP Lab Session 3 Student name: Page 1
- Search Search Strategies for Scheduling Constraint-based Scheduling
- Foundations of Artificial Intelligence Introduction
- Genetic Algorithms To provide a background and understanding of basic genetic
- Foundations of Artificial Intelligence Neural Networks
- Foundations of Artificial Intelligence Introduction to Data Mining
- Game Playing Garry Kasparov and Deep
- Foundations of Artificial Intelligence Case Based Reasoning
- Artificial Intelligence Methods rxq@cs.nott.ac.uk
- Artificial Intelligence Methods rxq@cs.nott.ac.uk
- Artificial Intelligence Methods rxq@cs.nott.ac.uk
- Artificial Intelligence Methods rxq@cs.nott.ac.uk
- 2009/2010 G52AIM Assessment Feedback Coursework Feedback
- Decision Support Methodologies E-mail: rxq@cs.nott.ac.uk
- Decision Support Methodologies E-mail: rxq@cs.nott.ac.uk
- Decision Support Methodologies E-mail: rxq@cs.nott.ac.uk
- Decision Support Methodologies E-mail: rxq@cs.nott.ac.uk
- Constraint Logic Programming Modeling CSPs Case Study I
- Constraint Logic Programming Modeling CSPs Case Study II
- Constraint Logic Programming Constraint Optimisation Problems
- Z Notations Dr. Rong Qu
- Schema Algebra Dr. Rong Qu
- Artificial Intelligence Search School of Computer Science
- Introduction The Formulation The Hybrid Approach Computational Experiment Results Conclusions A Hybrid Constraint Programming
- PlanSIG 2006 University of Nottingham, UK, 14th
- A Hyperheuristic Approach to Scheduling a Sales Peter Cowling, Graham Kendall, Eric Soubeiga
- Guided Operators for a Hyper-Heuristic Genetic Limin Han, Graham Kendall
- Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at University
- School of Computer Science and Information Technology University of Nottingham
- Motivations for MADbot: a Motivated And Goal Directed Robot Alexandra Coddington
- Dr. Rong Qu rxqdsFnottFFuk
- PlanSIG'2006 The 25th Workshop of the UK PLANNING AND SCHEDULING
- Constraint Logic Programming Solving 8-Queen Puzzle Demo
- The Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK
- 2ASAP Group, The University of Nottingham Intelligent Decision Support methodologies for real
- Artificial Intelligence Programming
- An Enhanced Weighted Graph Model for Examination/Course Timetabling
- An investigation on a hyper-heuristic upon graph heuristics for timetabling problems
- Introduction to Artificial Intelligence Blind Searches
- Foundations of Artificial Intelligence Knowledge Representation and Reasoning
- Constraint Logic Programming Constraint Satisfaction Problems
- Constraint Logic Programming Modeling CSPs Case Study I
- 2009/2010 G52AIP Assessment Feedback 2009/2010 G52AIP Exam Feedback
- The Z Notation: A Reference Manual
- Foundations of Artificial Intelligence Uncertainty
- G53CLP Lab Session 2 Student name: Page 1
- Artificial Intelligence Methods rxq@cs.nott.ac.uk
- Problems, Problem Spaces and Search Foundations of Artificial Intelligence
- Fuzzy Multiple Ordering Criteria for Examination Timetabling
- School of Computer Science G53FSP Formal Specifications 1 Formal Specification
- Introduction to Artificial Intelligence (G51IAI)
- School of Computer Science G53FSP Formal Specification 1 Formal Specification
- Constraint Logic Programming Basic Search Strategies
- Network Flow Models for Intraday Personnel Scheduling Problems
- Artificial Intelligence Methods rxq@cs.nott.ac.uk
- G53CLP Lab Session 1 Student name: Page 1
- 2009/2010 G51IAI Assessment Feedback Coursework Feedback
- Artificial Intelligence Search Methodologies
- Artificial Intelligence Search School of Computer Science
- Foundations of Artificial Intelligence Uncertainty and Reasoning
- Foundations of Artificial Intelligence Knowledge Acquisition
- Foundations of Artificial IntelligenceFoundations of Artificial Intelligence Introduction
- Introduction to Artificial Intelligence Discuss what is meant by Artificial Intelligence (AI)
- Constraint Logic Programming Overview of CP
- Evaluation of the University Course Timetabling Problem with the Linear Numberings Method
- Constraint Logic Programming Search Orders in CSP
- 2010/2011 G53CLP Assignment Student name: Page 1
- Algorithms for radiotherapy treatment booking Sanja Petrovic*
- A Hybrid Scatter Search Meta-heuristic for Delay-constrained Multicast Routing Problems
- PlanSIG 2006, Nottingham, UK December 2006
- G53CLP Lab Supplement Exercises Student name: Page 1
- A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine
- Decision Support Methodologies E-mail: rxq@cs.nott.ac.uk
- Dr Rong Qu 1 Decision Support Methodologies
- GRANULAR MODELLING OF EXAM TO SLOT ALLOCATION Siti Khatijah Nor Abdul Rahim1
- A simulated annealing based hyperheuristic ... Dowsland et. al (20/12/2004) 1 A simulated annealing based hyperheuristic for determining shipper sizes
- Problems, Problem Spaces and Search Foundations of Artificial Intelligence Problems, Problem Spaces and Search
- Foundations of Artificial Intelligence Knowledge Representation
- Foundations of Artificial Intelligence Knowledge Acquisition
- Genetic Algorithms To provide a background and understanding of basic genetic
- Beyond the Job-Shop: Scheduling in the Real World It is a curious feature of scheduling as an applied discipline that the problems whose
- Introduction to Artificial Intelligence Game Playing
- Knowledge Discovery in Hyper-Heuristic Using Case-Based Reasoning for Timetabling
- Functions and Sequences Dr. Rong Qu
- Machine Scheduling Timetabling Employees Scheduling Constraint-based Scheduling: Modeling
- Constraint Logic Programming Search Orders
- Case-Based Reasoning in Course Timetabling Problems
- European Journal of Operational Research, 176: 177-192, 2007. A Graph-Based Hyper-Heuristic for Educational
- A new bi-objective evolutionary algorithm using clustering heuristics to solve the Multi-mode Resource-Constrained Project Scheduling Problem
- Particle Swarm Optimization for the Steiner Tree in Graph and Delay-Constrained Multicast Routing Problems
- Hybridising ConstructiveHeuristics within Hyper-heuristic Frameworks
- 2010/2011 G51IAI Revision Guidance First of all, students are encouraged to have a look at the feedback to G51IAI exam 2010/2011 at
- Linear Combinations of Heuristics for Examination Timetabling
- An Adaptive Tie Breaking and Hybridisation Hyper-Heuristic for Exam Timetabling
- Title: A Compact Genetic Algorithm for the Network Coding Based Resource
- IEEE COMMUNICATIONS LETTERS, ACCEPTED FOR PUBLICATION 1 A Population Based Incremental Learning for
- 2010/2011 G53CLP Assessment Feedback Overall Assessment
- A Graph Based Hyper-heuristic -research issues and extensions
- 2010/2011 G64FAI Assessment Feedback Statistics for each question
- 2010/2011 G51IAI Assessment Feedback Statistics for each question
- An Iterative Local Search Approach based on Fitness Landscapes Analysis for the Delay-constrained Multicast
- A Honey-bee Mating Optimization Algorithm for Educational Timetabling Problems
- -g51iai -Module Introduction to Artificial Intelligence Group I: Wednesday 22nd Feb 10am-11am, A32
- 2011/2012 G51IAI Lab Session Exercise: Implementing XOR in Matlab
- Particle Swarm Optimization for the Steiner Tree in Graph and Delay-Constrained Multicast Routing Problems
