Motivation-Based Selection of Negotiation Opponents Steve Munroe and Michael Luck IAM Group University of Southampton.

Slides:



Advertisements
Similar presentations
MULTI-AGENT BASED SCHEDULING D. Ouelhadj ASAP (Automated Scheduling Optimisation and Planning) Research Group School of Computer Science and IT University.
Advertisements

Modelling with expert systems. Expert systems Modelling with expert systems Coaching modelling with expert systems Advantages and limitations of modelling.
Benefit Transfer of Non-Market Values – Understanding the concepts John Rolfe Central Queensland University.
Reaching Agreements II. 2 What utility does a deal give an agent? Given encounter  T 1,T 2  in task domain  T,{1,2},c  We define the utility of a.
Workpackage 2: Norms
Agents, Power and Norms Michael Luck, Fabiola López y López University of Southampton, UK Benemérita Universidad Autonoma de Puebla, Mexico.
Architecture Representation
15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois Intelligent Agents in Design Zbigniew Skolicki Tomasz.
15 THEORY OF GAMES CHAPTER.
Designing Scoring Rubrics. What is a Rubric? Guidelines by which a product is judged Guidelines by which a product is judged Explain the standards for.
Planning using Problem Analysis and The Theory of Change.
Design Research Intelligent questioning for effective designs.
Basic guidelines for the creation of a DW Create corporate sponsors and plan thoroughly Determine a scalable architectural framework for the DW Identify.
Effective Coordination of Multiple Intelligent Agents for Command and Control The Robotics Institute Carnegie Mellon University PI: Katia Sycara
Analyzing the tradeoffs between breakup and cloning in the context of organizational self-design By Sachin Kamboj.
The Rational Decision-Making Process
Agent Mediated Grid Services in e-Learning Chun Yan, Miao School of Computer Engineering Nanyang Technological University (NTU) Singapore April,
A Heuristic Bidding Strategy for Multiple Heterogeneous Auctions Patricia Anthony & Nicholas R. Jennings Dept. of Electronics and Computer Science University.
Quality of Service in IN-home digital networks Alina Albu 23 October 2003.
Extensions to Consumer theory Inter-temporal choice Uncertainty Revealed preferences.
Stochastic Models in Planning Complex Engineer-To-Order Products
Distributed Rational Decision Making Sections By Tibor Moldovan.
Planning operation start times for the manufacture of capital products with uncertain processing times and resource constraints D.P. Song, Dr. C.Hicks.
MT 340: Quantitative Methods Dr. Caulk Quantitative Decision Making 7 th ed by Lapin and Whisler.
1 Enviromatics Decision support systems Decision support systems Вонр. проф. д-р Александар Маркоски Технички факултет – Битола 2008 год.
Copyright c 2006 Oxford University Press 1 Chapter 4 Group Tasks and Activities Wide variety of synonyms and metaphors for groups and teams Crosses context.
Design process. Design briefs Investigating Designing Producing Analysing and evaluating Design process wall charts.
CHAPTER 22 Management-Control Systems, Transfer Pricing,
Name the five marketing strategies that make up the marketing mix.
Value Judgment of the Sense of Security for Nursing Care Robots Based on the Prospect Theory under Uncertainty Hiroyuki Tamura 1 and Yoshitomo Miura 2.
Principled Negotiation 4 Scholars from the Harvard Negotiation Project have suggested ways of dealing with negotiation from a cooperative and interest-
Honoraria for Design Build Projects Methodology Assessment Matrix.
Instructional Design Eyad Hakami. Instructional Design Instructional design is a systematic process by which educational materials are created, developed,
Supported by Offer Construction for Generators with Inter-temporal Constraints via Markovian DP and Decision Analysis Grant Read, Paul Stewart Ross James.
Quantitative Decision Making and Risk Management CS3300 Fall 2015.
7-2 Decision Making: How Individuals and Groups Arrive at Decisions Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.
Chapter 10 Contemporary Project Management Kloppenborg
7-1 Copyright © 2009 Pearson Education Canada CHAPTER 7 Media Planning Essentials.
An Integration Framework for Sensor Networks and Data Stream Management Systems.
TOWARDS A TRUST MODEL IN E-LEARNING: ANTECEDENTS OF A STUDENT’S TRUST W Wongse-ek, G B Wills, L Gilbert.
FOUNDATIONS OF INDIVIDUAL BEHAVIOR Biographical characteristics and ability affect employee’s performance (productivity, absence, turnover) and satisfaction,
Plan-Directed Architectural Change for Autonomous Systems Daniel Sykes, William Heaven, Jeff Magee, Jeff Kramer Imperial College London.
Linear Programming An Example. Problem The dairy "Fior di Latte" produces two types of cheese: cheese A and B. The dairy company must decide how many.
1 Chapter 7 Applying Simulation to Decision Problems.
Department of Electronic Engineering Challenges & Proposals INFSO Information Day e-Infrastructure Grid Initiatives 26/27 May.
All Rights Reserved to Kardan University 2014 Kardan University Kardan.edu.af.
Xiao Liu 1, Yun Yang 1, Jinjun Chen 1, Qing Wang 2, and Mingshu Li 2 1 Centre for Complex Software Systems and Services Swinburne University of Technology.
QUANTITATIVE TECHNIQUES
Chapter 19 Appendix: Indifference Curves McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. 13e.
Modelling the Process and Life Cycle. The Meaning of Process A process: a series of steps involving activities, constrains, and resources that produce.
12-CRS-0106 REVISED 8 FEB 2013 APO (Align, Plan and Organise)
MODULE 9 MANAGERS AS DECISION MAKERS “Decide first, then act” How do managers use information to make decisions and solve problems? What are the steps.
Decision Making Matrix A Closer Look at Preliminary Ideas.
Evaluating Dynamic Services in Bioinformatics Maíra R. Rodrigues Michael Luck University of Southampton, UK Tenth International Workshop CIA 2006, Edinburgh.
An Architecture-Centric Approach for Software Engineering with Situated Multiagent Systems PhD Defense Danny Weyns Katholieke Universiteit Leuven October.
University of Papua New Guinea Principles of Microeconomics Lecture 13: Oligopoly.
CREATIVITY AND THE BUSINESS IDEA Chapter : 05 CREATIVITY AND THE BUSINESS IDEA
TEACHING STYLES TEACHING STYLES. LEARNING OUTCOMES To examine different teaching styles To evaluate how teaching styles can affect performance To begin.
Copyright © Houghton Mifflin Company. All rights reserved. 13–1 Stages for Establishing Prices FIGURE 13.1.
Designing Scoring Rubrics
Decision Analysis Objective
Chapter 3 Supply Chain Drivers and Obstacles
DSS & Warehousing Systems
TÆMS-based Execution Architectures
Basic Management Functions
SUPPLY & DEMAND.
Competitive Industry Report and Calculations
Decision Analysis Objective
CONTROL A process of monitoring and correcting subordinates performance to achieve organizational goals.
Honoraria for Design Build Projects Methodology Assessment Matrix
Presentation transcript:

Motivation-Based Selection of Negotiation Opponents Steve Munroe and Michael Luck IAM Group University of Southampton

Presentation Outline  The problem: dynamic domains and negotiation  Motivated agents for dynamic domains  Negotiation goals  Selecting opponents

Autonomous Negotiation  Three phases of negotiation  Dealing with dynamics and uncertainty  Strategies / Tactics / Protocols  Agent driven  Pre-negotiation: Where do the issues come from? How are reservation values determined? Who best to negotiate with?

Autonomy and Motivation Autonomy: The ability to make decisions and select courses of action that further one’s own interests based on one’s own assessment of the situation Motivation: Any desire or preference that can lead to the generation and adoption of goals and that affects the outcome of the reasoning or behavioural task intended to satisfy those goals Motivation >>>> Utility

A Motivated Agent Architecture  Motivational Cues  Intensity  Mitigation

The Warehouse Domain  Controller agent must negotiate with delivery agent about moving boxes around the warehouse

Negotiation Goals  Dynamic reconfiguration of issues to meet current demands Fixed attributes Potential attribute

Negotiation Goals - 2  Two types of potential attributes Non resource-based Resource-based

Non Resource-Based Attributes: constructing preferences  What is the structure of the preference?  Determined by assessing each possibility in terms of motivational worth

Non Resource-Based Attributes: generating preferences  Calculates the worth of each of the possible values that can be used to instantiate a potential attribute : v x gs x ms [0,1]

Non Resource-Based Attributes: attribute classification rules  Fixed if the preferences of the agent contains at most one value that has positive motivational worth and all the rest have negative motivational worth.  Negotiable if the preferences of the agent contains more than one value that has positive motivational worth.  Slack if all the values contained in the agent’s preferences have the same motivational worth (both positive or negative).

Non Resource-Based Attributes: example preferences

Resource-Based Attributes: dynamic constraints  Dynamic evaluation of resource use  Never slack!  Preference structure is monotonic  Problem is to determine reservation on the use of a resource

Resource-Based Attributes: the reservation manager  Calculate the unit worth of a resource, r, for an agent, a, UW a r =  r ·bw a r where  r = I m r  [-1,1]  Then obtain the quantity of resource whose (negative) worth is equal to the worth of the goal.

Negotiation Goal Structure  A negotiation goal contains sets of Fixed attributes Negotiable attributes Slack attributes Reservation values for resource- dependent attributes

Opponent Selection  Selecting to minimise conflict  Selecting to optimise resource use

Selecting to Minimise Conflict  Each agent selects its own negotiable attributes  Intersection of choices defines issues  Smaller intersections means less conflict (easier negotiation)

The Conflict Minimisation Selection Mechanism  Issue analyser calculates the expected intersection size of this agent’s issues and opponent’s issues  Uses attribute selection frequency information about the opponent

Selecting To Optimise Resource Use  Resource manager determines the expected deal price of an opponent  Checks to see if the expected deal price is below reservation  Price profiles  Concessionary flexibility

Combining the Mechanisms  Selection based on both conflict minimisation and resource optimisation  Motivationally weighted by the worth of the goal and the worth of the resource

Preliminary Evaluation  Tested only on price minimisation  Compare opponent selection against optimal selection  Agent learns to select the optimal

Conclusions  Little autonomy apparent in pre-negotiation stage  Motivation enables autonomous decision- making in dynamic negotiation settings  New model of negotiation goals gives scope for motivation-based dynamic decision- making  Characteristics of negotiation goal guides opponent selection.

End of Presentation… Questions?