1 Multi-Agent Planning. 2 Tasks as Agents negotiating for resources Self-interested Selfish Agents Complexity of Negotiation Tasks Abstraction & Analysis.

Slides:

Advertisements

Similar presentations

1 Planning 2 Some material adapted from slides by Tim Finin,Jean-Claude Latombe, Lise Getoor, and Marie desJardins.

Advertisements

1 Integration of Artificial Intelligence and Operations Research Techniques for Combinatorial Problems Carla P. Gomes Cornell University

MODELLING OF MOBILE AGENT SYSTEMS USING dynPLA APPROACH Agnė Paulauskaitė-Tarasevičienė Henrikas Pranevičius 1.

Foundations of Constraint Processing CSP 101: continued1 Foundations of Constraint Processing CSCE421/821, Spring

PlanSIG, Dec, Temporal Plans and Resource Management Pieter Buzing & Cees Witteveen Delft University of Technology.

CoreGRID Workpackage 5 Virtual Institute on Grid Information and Monitoring Services Authorizing Grid Resource Access and Consumption Erik Elmroth, Michał.

Analyzing the tradeoffs between breakup and cloning in the context of organizational self-design By Sachin Kamboj.

Planning II GraphPlan and Russell and Norvig: ch.12 CMSC421 – Fall 2006 based on material from Jim Blythe, JC Latombe, Marie desJardins and Daphne Koller.

Resource Management – a Solution for Providing QoS over IP Tudor Dumitraş, Frances Jen-Fung Ning and Humayun Latif.

Foundations of Constraint Processing, Fall 2005 September 12, 2005CSP 101: continued1 Foundations of Constraint Processing CSCE421/821, Fall 2005:

استاد درس : دکتر عبداله زاده درس : هوش مصنوعی توزیع شده ارائه کننده : مریم باحجب ایمانی.

© Franz Kurfess Project Topics 1 Topics for Master’s Projects and Theses -- Winter Franz J. Kurfess Computer Science Department Cal Poly.

Lock Inference for Systems Software John Regehr Alastair Reid University of Utah March 17, 2003.

DAME Architecture Hybrid distributed data mining model Integrates the client-server and mobile agent paradigms Adopting the most suitable approach for.

1RADAR – Scheduling Task © 2003 Carnegie Mellon University RADAR – Scheduling Task May 20, 2003 Manuela Veloso, Stephen Smith, Jaime Carbonell, Brett Browning,

Wireless Distributed Sensor Tracking: Computation and Communication Bart Selman, Carla Gomes, Scott Kirkpatrick, Ramon Bejar, Bhaskar Krishnamachari, Johannes.

Distributed Rational Decision Making Sections By Tibor Moldovan.

Ekaterina Smorodkina and Dr. Daniel Tauritz Department of Computer Science Power Grid Protection through Rapid Response Control of FACTS Devices.

Multirobot Coordination in USAR Katia Sycara The Robotics Institute

BNAIC, Oct, Temporal Plans and Resource Management Pieter Buzing & Cees Witteveen TU Delft.

Distinguishing ICT Literacy from Information Literacy Philip Candy NHSU Institute

Agents Computer Programs of a certain type Effectively bodiless robots –Rise of internet enables Agents Lostness –As life becomes more complex, we cannot.

A preliminary analysis of AAAI-99 submissions Devika Subramanian Rice University.

1 Endgame Logistics  Final Project Presentations  Tuesday, March 19, 3-5, KEC2057  Powerpoint suggested ( to me before class)  Can use your own.

Panel Abstractions for Large-Scale Distributed Systems Henri Bal Vrije Universiteit Amsterdam.

Agent architectures Smarter software for astronomers Alasdair Allan University of Exeter, Exeter, U.K.

ANTs PI meeting, May 29-31, 2002Washington University / DCMP1 Flexible Methods for Multi-agent Distributed Resource Allocation by Exploiting Phase Transitions.

L ９ : Collaborations Why? Terminology Coherence Coordination Reference ｓ :

Travis Steel. Objectives What is the Agent Paradigm? What is Agent-Oriented Design and how is it different than OO? When to apply AOD techniques? When.

ANTs PI Meeting, Nov. 29, 2000W. Zhang, Washington University1 Flexible Methods for Multi-agent distributed resource Allocation by Exploiting Phase Transitions.

DOMENICO TALIA (joint work with M. Cannataro, A. Congiusta, P. Trunfio) DEIS University of Calabria ITALY Grid-Based Data Mining and.

Using Transactional Workflow Ontology in Agent Cooperation J. Korhonen, L. Pajunen, and J. Puustjärvi.

The Anatomy of the Grid Mahdi Hamzeh Fall 2005 Class Presentation for the Parallel Processing Course. All figures and data are copyrights of their respective.

Embedding Constraint Satisfaction using Parallel Soft-Core Processors on FPGAs Prasad Subramanian, Brandon Eames, Department of Electrical Engineering,

These slides are designed to accompany Web Engineering: A Practitioner’s Approach (The McGraw-Hill Companies, Inc.) by Roger Pressman and David Lowe, copyright.

Topic 8: Coordination and Control Applications coordination and control applications a reference architecture ant-based coordination and control.

Director: Prof. Maja J Matarić Associate Director: Prof. Gaurav S. Sukhatme Founder: Prof. George A. Bekey G OALS Automate the process of robot controller.

Automated Mechanism Design Tuomas Sandholm Presented by Dimitri Mostinski November 17, 2004.

Riga Technical University Department of System Theory and Design Usage of Multi-Agent Paradigm in Multi-Robot Systems Integration Assistant professor Egons.

1 Object Oriented Logic Programming as an Agent Building Infrastructure Oct 12, 2002 Copyright © 2002, Paul Tarau Paul Tarau University of North Texas.

1 Cooperative multi-agent systems In cooperative MAS agents strive to reach a common goal and increase the combined utility of their actions Limitations.

Static WCET Analysis vs. Measurement: What is the Right Way to Assess Real-Time Task Timing? Worst Case Execution Time Prediction by Static Program Analysis.

DISTIN: Distributed Inference and Optimization in WSNs A Message-Passing Perspective SCOM Team

PnP Networks Self-Aware Networks Self-Aware Networks Self-Healing and Self-Defense via Aware and Vigilant Networks PnP Networks, Inc. August, 2002.

Robust Planning using Constraint Satisfaction Techniques Daniel Buettner and Berthe Y. Choueiry Constraint Systems Laboratory Department of Computer Science.

1 CMSC 471 Fall 2004 Class #21 – Thursday, November 11.

Energy-Efficient Signal Processing and Communication Algorithms for Scalable Distributed Fusion.

1 CMSC 471 Fall 2002 Class #24 – Wednesday, November 20.

Distributed Systems1 Message Passing vs. Distributed Objects  The message-passing paradigm is a common model for distributed computing, in the sense that.

An Architecture-Centric Approach for Software Engineering with Situated Multiagent Systems PhD Defense Danny Weyns Katholieke Universiteit Leuven October.

Adaptive Reinforcement Learning Agents in RTS Games Eric Kok.

Training Believable Agents In 3D Electronic Business Environments Using Recursive-Arc Graphs Anton Bogdanovych University of Western Sydney, Australia.

Towards middleware based situation awareness Leo Motus, Merik Meriste ja Jürgo-Sören Preden.

Next Generation of Apache Hadoop MapReduce Owen

Requirements Engineering Processes, York EngD Programme, 2009Slide 1 Requirements engineering processes Prof Ian Sommerville.

Formal Complexity Analysis of RoboFlag Drill & Communication and Computation in Distributed Negotiation Algorithms in Distributed Negotiation Algorithms.

Comparison on Size FreeRTOS RTLinux Kernel Size Kernel Size

The Hebrew University of Jerusalem School of Engineering and Computer Science Academic Year: 2011/2012 Instructor: Jeff Rosenschein.

1 Phase 1 Problems ● Small size – 20 to 150 methods – 10 to 20 agents ● Problem Classes – One static + one dynamically arriving problem – Uncertainty in.

Back to Basics with Project, Part 1 Carl Chatfield, PMP February, 2007 Puget Sound MPA.

TÆMS-based Execution Architectures

Jingyong Liu*, Lichen Zhang,

Gabor Madl Ph.D. Candidate, UC Irvine Advisor: Nikil Dutt

این دوره شامل: تعریف مذاکره کلمات کلیدی ورودی ها انواع مذاکره

Class #20 – Wednesday, November 5

Market-based Dynamic Task Allocation in Mobile Surveillance Systems

Class #17 – Tuesday, October 30

Year 12 Sociology Induction Task

Presentation transcript:

1 Multi-Agent Planning

2 Tasks as Agents negotiating for resources Self-interested Selfish Agents Complexity of Negotiation Tasks Abstraction & Analysis I need resource X …

3 Tasks as Agents negotiating for resources Self-interested Selfish Agents Self-interested Collaborating Agents Complexity of Negotiation Tasks Abstraction & Analysis I need resource X … I need resource X, and it will enable other agents to use it later … I need resource X, and it will enable other agents to use it later …

4 Tasks as Agents negotiating for resources Self-interested Selfish Agents Self-interested Collaborating Agents Global Welfare Oriented Collaborating Agents Complexity of Negotiation Tasks Abstraction & Analysis I need resource X … I need resource X, and it will enable other agents to use it later … I need resource X, and it will enable other agents to use it later … I need resource X, and it will allow us to improve our joint success … I need resource X, and it will allow us to improve our joint success …

5 Tasks as Agents negotiating for resources Self-interested Selfish Agents Self-interested Collaborating Agents Global Welfare Oriented Collaborating Agents Complexity of Negotiation Tasks Abstraction & Analysis Distributed Constraint Satisfaction Distributed Planning with Conjunctive Goals Distributed (Hierarchical) Planning with Disjunctive Goals Technology Reusage

6 Distributed Constraint Satisfaction Distributed Planning with Conjunctive Goals Distributed (Hierarchical) Planning with Disjunctive Goals Formal Modeling & Complexity Analysis More and more important! As the negotiation systems are getting more complex, we need more and more advanced: Formal problem modeling & complexity analysis, Structural analysis, and Development of scalable generic negotiation protocols

Exploiting Structure is Crucial! Poly- time NP-completePSPACE-completeEXPTIME-complete Scheduling In terms of worst-case computational complexity, mixed scheduling/planning is significantly harder than scheduling. Therefore, need to exploit problem structure to tame computational complexity. Hybrid Scheduling & Planning

Example (Nasa) Planning is hard: find right sequence of actions 10 actions, 10! = 3 x 10 6 Contingency planning is really hard: possible plans! 10 x 9 2 x 8 4 x 7 8 x … x 2 256