Coordination of FIPA compliant software agents using utility function assignment Steven James Lynden A dissertation submitted in partial fulfilment of the requirements of Cardiff University for the degree of Doctor of Philosophy School of Computer Science Cardiff University June, 2004 UMI Number: U584690 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. Dissertation Publishing UMI U584690 Published by ProQuest LLC 2013. Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code. ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 1 DECLARATION This work has not previously been accepted in substance for any degree and is not concurrently submitted in candidature for any degree. Signed .................... (candidate) Date ....2-£(. .Q.r..* 2 0 .0 ... STATEMENT 1 This thesis is the result of my own investigations, except where otherwise stated. Other sources are acknowledged by explicit references. A bibliography is appended. Signed ........ .... (candidate) D ate .2^...7...Q.4?..7!.T^Q.4£.. STATEMENT 2 I hereby give consent for my thesis, if accepted, to be available for photocopying and for inter-library loan, and for the title and summary to be made available to outside organisations. Signed . .... (candidate) D ate .... 2 ABSTRACT A Multiagent System (MAS) consisting of interacting autonomous agents moti­ vated by individual objectives may be utilised to achieve global objectives in sce­ narios where centralised control is very difficult because of the distributed nature, complexity, or other restrictions present in a problem domain. When deploying MAS to achieve global objectives, a degree of coordination is usually required in order to ensure that the behaviour of the system is desirable with respect to these objectives. A sub-optimisation problem occurs when individual agent objectives are inconsis­ tent with global objectives. Current approaches towards this problem within the context of learning based agents include configuring the utility functions possessed by individual agents so that the maximisation of individual utility functions results in a desired global behaviour. Interoperability is of critical importance in Internet- scale MAS, and a promising standard for this is currently evolving in the form of the “Foundation for Intelligent Physical Agents” (FIPA) specifications. This thesis fo­ cuses on the integration of techniques based on the assignment of utility functions in order to coordinate MAS within the domain of FIPA compliant agent systems. The notion of utility is extended to form two separate types: performance and functional utilities. Whereas functional utility is based on abstract application specific objec­ tives, performance utility concentrates on performance engineering related issues. The benefit of this approach is demonstrated by a software toolkit supporting the development of learning based FIPA compliant MAS, which is applied within two domains, an application based on market based buyer agents in artificial markets, and a computational Grid based application. Contents 1 Introduction 13 1.1 Agents, multiagent systems, and coordination ....................................... 13 1.2 Novel contributions of this thesis ............................................................. 15 1.2.1 Hypothesis.......................................................................................... 17 1.3 Structure of this thesis ................................................................................ 18 2 Multiagent system development techniques 20 2.1 Introduction ................................................................................................... 20 2.2 Multiagent coordination ............................................................................. 20 2.2.1 Learning and coordination ............................................................. 24 2 .2.2 Collective Intelligence ....................................................................... 25 2.3 Agent interoperability ............................................................................... 28 2.3.1 OMG M A S IF .................................................................................... 30 2.3.2 K Q M L ................................................................................................ 31 2.3.3 F IP A ................................................................................................... 32 2.3.4 Evaluation of agent interoperability sta n d a rd s .......................... 36 2.4 Implementing multiagent system s ............................................................. 37 2.4.1 Agent development toolkits .......................................................... 37 2.5 C onclusions ................................................................................................... 39 3 CONTENTS 4 3 Utility function assignment 41 3.1 Introduction ................................................................................................... 41 3.2 U tility ............................................................................................................ 42 3.3 LEAF - The Learning Agent FIPA Compliant Community Toolkit . 43 3.3.1 Global utility functions .................................................................... 50 3.3.2 Local utility fu n c tio n s .................................................................... 50 3.4 Observable properties, remote parameters, and utility function as­ signment 51 3.4.1 Observable properties ....................................................................... 51 3.4.2 Remote param eters.......................................................................... 52 3.4.3 Performance utility functions ................................................ 54 3.5 Utility function assignment and computation .......................................... 54 3.5.1 ESN b e h av io u r ................................................................................ 54 3.5.2 Aggregating utility functions.......................................................... 56 3.5.3 Agent behaviour ............................................................................. 56 3.5.4 Communication m echanism s .......................................................... 58 3.6 Conclusion ...................................................................................................... 59 3.6.1 The benefits of utility function assignment .................................. 59 3.6.2 Assumptions concerning agent behaviours ................................... 59 3.6.3 Performance U tility .......................................................................... 60 4 Implementation of the LEAF toolkit 61 4.1 Requirements analysis ................................................................................ 61 4.1.1 Requirements definition ................................................................. 62 4.1.2 Requirements specification ............................................................. 62 4.2 Choice of programming language .............................................................. 63 4.3 Decision to utilise the FIPA-OS agent toolkit .......................................... 64 4.3.1 Developing agents using FIPA-OS ............................................... 64 4.4 LEAF architecture ...................................................................................... 66 4.4.1 LEAF a g e n ts................................................................................... 68 4.4.2 LEAF ta s k s ....................................................................................... 80 CONTENTS 5 4.4.3 Utility functions ................................................................................ 80 4.4.4 LEAF E S N ...................................................................................... 83 4.5 Developing MAS using the LEAF to o lk it .............................................. 84 4.6 Conclusion .................................................................................................... 87 5 Agent learning in LEAF communities 88 5.1 Introduction ................................................................................................. 88 5.2 System architecture..................................................................................... 89 5.2.1 Seller agents...................................................................................... 90 5.2.2 Buyer agents ................................................................................... 92 5.3 Utility function assignment ........................................................................ 94 5.3.1 Global utility fu n c tio n ................................................................... 94 5.3.2 Local utility fu n c tio n s ................................................................... 94 5.3.3 Observable properties and remote pa ra m e te rs ........................... 95 5.3.4 Summary of system behaviour ...................................................... 95 5.4 Results and observations ........................................................................... 96