We provide a brief description of the Python-DTU system, including the
overall design, the tools and the algorithms that we plan to use in the agent
contest.Comment: 4 page

Ettienne, Mikko Berggren

Vester, Steen

Villadsen, Jørgen

English

arXiv

We provide a brief description of the Python-DTU system, including the overall design, the tools and the algorithms that we plan to use in the agent contest

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 19, 2017Multi-Agent Programming Contest 2011 - The Python-DTU TeamVilladsen, Jørgen; Ettienne, Mikko Berggren; Vester, SteenPublication date:2011Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Villadsen, J., Ettienne, M. B., & Vester, S. (2011). Multi-Agent Programming Contest 2011 - The Python-DTUTeam. Technical University of Denmark (DTU).arXiv:1110.0105v1  [cs.MA]  1 Oct 2011Multi-Agent Programming Contest 2011—The Python-DTU TeamJørgen Villadsen, Mikko Berggren Ettienne, and Steen VesterDepartment of Informatics and Mathematical ModellingTechnical University of DenmarkRichard Petersens Plads, Building 321, DK-2800 Kongens Lyngby, DenmarkAbstract. We provide a brief description of the Python-DTU system,including the overall design, the tools and the algorithms that we planto use in the agent contest.Updated 1 October 2011: Appendix with comments on the contest added.Introduction1. The name of our team is Python-DTU. We participated in the contest in 2009and 2010 as the Jason-DTU team [2,3], where we used the Jason platform[1], but this year we use just the programming language Python.We intend in a later paper to elaborate on the reasons for abandoning theJason platform and its agent-oriented programming language AgentSpeak.2. The members of the team are as follows:– Jørgen Villadsen, PhD– Mikko Berggren Ettienne, MSc student (new in the team this year)– Steen Vester, MSc studentWe are affiliated with DTU Informatics (short for Department of Informaticsand Mathematical Modelling, Technical University of Denmark, and locatedin the greater Copenhagen area).3. The main contact is associate professor Jørgen Villadsen, DTU Informatics,email: jv@imm.dtu.dk4. We expect that we will have invested approximately 400 man hours whenthe tournament starts.1System Analysis and Design1. The competition is built on the Java MASSim platform and the Java EIS-MASSim framework is distributed with the competition files. This frameworkis based on EIS and abstracts the communication between the server andthe agents to simple Java method calls and callbacks.To utilize this framework we started out with the Java implementation ofPython called Jython which in contrast to Python can import Java librariesand classes. To support agent communication in our multi-agent systemwe have so far used the Apache ActiveMQ as a messaging server whichoffers clients for all popular programming languages. Using the EISMASSimJava framework together with ActiveMQ clients written in Python gluingit all together with Jython gave some performance issues when exchangingpercepts between the agents.We found that each component performed well tested in a controlled contextwhich suggested that the issues were related to the interaction between thecomponents.We decided to skip Jython and EISMASSim to instead follow a much cleanerPython-only implementation. Even though some work was needed to imple-ment the protocol specific parts which EISMASSim handled, this left us witha more flexible implementation of which we have complete knowledge andcontrol of every part of the implementation. It also solved the performanceissues related to component interaction.We also plan to skip the messaging server and instead let the agents com-municate directly using a simple and efficient text-based protocol to furtherimprove the performance of the system. A simple text-based protocol allowsus to keep the distributivity and modularity. Thus it will still be posibleto use agents written in different programming languages. Furthermore, byimplementing our own message server, we can tweak the low-level featuresto suit the need of our specific system.2. We do not use any existing multi-agent system methodology.3. We do not plan to distribute the agents on several machines.4. We do not plan a solution with a centralization of coordination/informationon a specific agent. Rather we plan a decentralized solution where agentsshare percepts through messages and coordinate actions using distributedalgorithms.5. The team communication is based on the publisher-subscriber pattern. Ourmessage server has a number of topics to which agents can subscribe andpublish messages. This supports one-to-one communication and one-to-manycommunication in a simple way where the agents determine which topics tosubscribe to.26. To assign goals to agents we use a ring-based auction algorithm. Each roundin an auction includes nmessages where n is the number of agents participat-ing. Before participating in an auction all agents score their top n goals anduse this score to determine their bidding strategy. The algorithm terminateswhen all n agents are assigned to a unique goal.7. Each agent acts on its own behalf based on its local view of the world whichis updated through percepts and is thus autonomous and reactive. This isimplemented as an agent-control-loop in which the agents decide which ac-tions to execute based on their current view of the world. We have consideredimplementing an algorithm to determine the best way to parry a series ofattacks from a saboteur agent which would make the agents proactive.Software Architecture1. We implement the multi-agent system using just the programming languagePython.Even though we all have more experience with Java, we choose Python asour programming language, as we think it has some advantages over Java,mainly in development speed/programmer effectiveness. Some of the reasonsbeing that Python in contrast to Java:– is dynamically typed– is concise– is compact– supports multiple programming paradigms (object-oriented, imperative,functional)– is popular for scripting– does not need to be compiled before execution2. We use Python 2.7 on Ubuntu Linux and Mac OS X as the developmentplatforms and GEdit, Eclipse and TextMate as code editors/IDEs.3. As the runtime platform for the competition we plan to use a suitable Linuxsystem with Python 2.7.4. We plan to use the following algorithms:– All-pair shortest path extended to support dynamic vertex addition– Custom breadth-first-searches– Ring-based agreement algorithm3References1. Rafael H. Bordini, Jomi Fred Hu¨bner, and Michael Wooldridge. Programming Multi-Agent Systems in AgentSpeak Using Jason. John Wiley & Sons, 2007.2. Niklas Skamriis Boss, Andreas Schmidt Jensen, and Jørgen Villadsen. BuildingMulti-Agent Systems Using Jason. Annals of Mathematics and Artificial Intelli-gence, Springer Online First 6 May 2010.3. Steen Vester, Niklas Skamriis Boss, Andreas Schmidt Jensen, and Jørgen Villadsen.Improving Multi-Agent Systems Using Jason. Annals of Mathematics and ArtificialIntelligence, Springer Online First 5 March 2011.AcknowledgementsThanks to Niklas Christoffer Petersen, MSc student, for comments.More information about the Python-DTU team is available here:http://www.imm.dtu.dk/~jv/MASAppendixOur idea to skip the message server and implement our own text-based protocolwas slightly modified during the implementation phase. Instead of implementingour own message system, we reached the pragmatic conclusion to instead letthe agents communicate trough direct reference to a shared data structure. Thisallowed us to spend more time on other important issues and freed us from allperformance issues. The main reason for the message system idea was to preservemodularity and this could easily be implemented at a later state if time allowed.In the early development phase we suspected our dynamic all-pair shortestpath algorithm as a candidate for our performance issues. However we discoveredthat the issues were I/O related, and found that we had plenty of processing timeand power to perform multiple stock graph search algorithms at each simulationstep. This made our dynamic all-pair shortest path algorithm superfluous forthis scenario. However it could be highly relevant in case of less processing timeor bigger graphs.4

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