Learning and Cooperating Multi-Agent Scheduling Repair Using a Provenance-Centred Approach

QR Code

Learning and Cooperating Multi-Agent Scheduling Repair Using a Provenance-Centred Approach

The timetabling problem is to find a timetable solution by assigning time and resources to sessions that satisfy a set of constraints. Traditionally, research has focused on optimization towards a final solution but this paper focuses on minimizing disturbance impact due to changing conditions. A Mu...

Full description

Bibliographic Details
Main Authors:	Tan, Terence, Tan, Tele, West, Geoff, Low, S.
Other Authors:	Not known
Format:	Conference Paper
Published:	IEEE 2012
Online Access:	http://hdl.handle.net/20.500.11937/26518

Similar Items

A multi-agent based cooperative approach to scheduling and routing
by: Martin, Simon, et al.
Published: (2016)

An image engineering approach to analysing mobile mapping data
by: Borck, Michael, et al.
Published: (2014)

Computational Red Teaming for Physical Security Assessment
by: Tan, Terence Peng Lian, et al.
Published: (2014)

Breaking into BIM: Performing static and dynamic security analysis with the aid of BIM
by: Porter, Stuart, et al.
Published: (2014)

Provenance Ontology Model for Land Administration Spatial Data Supply Chains
by: Sadiq, Muhammad, et al.
Published: (2015)

A cooperative multi-agent approach in developing a knowledge based system for EIA
by: Jazzar, Moneef Mohammad Abdel-Kareem, et al.
Published: (1999)

A web deployed multi-agent based approach for student-lecturer appointment scheduling in institutions of higher learning
by: Anthony Jr, Bokolo, et al.
Published: (2019)

Provenance network analytics: an approach to data analytics using data provenance
by: Huynh, Trung Dong, et al.
Published: (2018)

Scale proportionate histograms of oriented gradients for object detection in co-registered visual and range data
by: Palmer, Richard, et al.
Published: (2012)

Using Depth to Extend Randomised Hough Forests for Object Detection and Localisation
by: Palmer, Richard, et al.
Published: (2013)

Post-disaster multi-period road network repair: work scheduling and relief logistics optimization
by: Li, S., et al.
Published: (2018)

Guilty repair sustains cooperation, angry retaliation destroys it
by: Skatova, Anya, et al.
Published: (2017)

Trustworthy provenance framework for document workflow provenance
by: Rupasinghe, P., et al.
Published: (2016)

Neural Network Preference Learning Approaches For Improving Agent-Based Meeting Scheduling Problems
by: Tang, En Lai
Published: (2007)

Visual maritime attention using multiple low-level features and Naive Bayes classification
by: Albrecht, Thomas, et al.
Published: (2011)

Cooperative multi agents for intelligent intrusion detection and prevention systems / Shahaboddin Shamshirband
by: Shamshirband, Shahaboddin
Published: (2014)

Cooperative power scheduling for wireless MIMO networks
by: Nokleby, M., et al.
Published: (2007)

Use of cooperative learning approaches in education
by: Kwok, Wai Jing
Published: (2008)

Intensity and Range Based Features for Object Detection in Mobile Mapping Data
by: Palmer, Richard, et al.
Published: (2012)

Using depth maps to find interesting regions
by: Borck, Michael, et al.
Published: (2014)

Improving agent-based meeting scheduling through preference learning.
by: Sulaiman, Md. Nasir, et al.
Published: (2009)

Cooperative Power Scheduling for a Network of MIMO Links
by: Dong, X., et al.
Published: (2010)

Distributed agents for online spatial searches
by: Gulland, Elizabeth-Kate, et al.
Published: (2016)

Cooperative multi-knowledge learning control system for obstacle consideration
by: Syafiq Fauzi, Kamarulzaman, et al.
Published: (2014)

Digital visual schedule and training system for centre of autistic children
by: Hitam, Salasiah, et al.
Published: (2011)

Recent research in cooperative path planning algorithms for multi-agent using mixed- integer linear programming
by: Che Ku, Nor Azie Hailma, et al.
Published: (2016)

Discovery of bioactive peptides as therapeutic agents for skin wound repair
by: Md Fadilah, Nur Izzah, et al.
Published: (2024)

Ethnopharmacological Approaches to Wound Repair
by: Akkol, Esra Küpeli, et al.
Published: (2013)

Deep learning approaches to aircraft maintenance, repair and overhaul: a review
by: Rengasamy, Divish, et al.
Published: (2018)

Quantitative assessment of 2D versus 3D visualisation modalities
by: Ting, Sarah, et al.
Published: (2011)

Deep reinforcement learning to multi-agent deep reinforcement learning
by: Samieiyeganeh, Mehdi, et al.
Published: (2022)

Multi cue performance evaluation metrics for tracking in video sequences
by: John, Gladis, et al.
Published: (2008)

Multi agent reinforcement learning for UAV collision avoidance
by: Abdul Hamid, Nor Asilah Wati, et al.
Published: (2024)

Multi-Agent Reinforcement Learning For Swarm Robots Formation
by: Bujang, Christina
Published: (2021)

From Canonical Face to Synthesis - An Illumination Invariant Face Recognition approach
by: Tan, Tele
Published: (2007)

Depositional age and provenance of the Cobar Supergroup
by: Parrish, M., et al.
Published: (2018)

A closed-loop EKF and multi-failure diagnosis approach for cooperative GNSS positioning
by: Liu, Haiying, et al.
Published: (2016)

Multi-model LPV approach to CSTR system identification with stochastic scheduling variable
by: Wei, J., et al.
Published: (2016)

Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR
by: Lin, Y., et al.
Published: (2016)

Multi-robot cooperation inspired by immune systems
by: Razali, Sazalinsyah, et al.
Published: (2009)

Cannot write session to /tmp/vufind_sessions/sess_ohfp0p1rs572a6volbqmuetvcl