%0 Conference Paper %1 1277347 %A Doherty, Darren %A O'Riordan, Colm %B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation %C London %D 2007 %E Thierens, Dirk %E Beyer, Hans-Georg %E Bongard, Josh %E Branke, Jurgen %E Clark, John Andrew %E Cliff, Dave %E Congdon, Clare Bates %E Deb, Kalyanmoy %E Doerr, Benjamin %E Kovacs, Tim %E Kumar, Sanjeev %E Miller, Julian F. %E Moore, Jason %E Neumann, Frank %E Pelikan, Martin %E Poli, Riccardo %E Sastry, Kumara %E Stanley, Kenneth Owen %E Stutzle, Thomas %E Watson, Richard A %E Wegener, Ingo %I ACM Press %K AI, Applications, Real-World agents, algorithms, analysis, artificial behaviour cooperative genetic intelligence, phenotypic programming, tactical team %P 1951--1958 %T A phenotypic analysis of GP-evolved team behaviours %U http://doi.acm.org/10.1145/1276958.1277347 %V 2 %X This paper presents an approach to analyse the behaviours of teams of autonomous agents who work together to achieve a common goal. The agents in a team are evolved together using a genetic programming (GP) 8 approach where each team of agents is represented as a single GP tree or chromosome. A number of such teams are evolved and their behaviours analysed in an attempt to identify combinations of individual agent behaviours that constitute good (or bad) team behaviour. For each team we simulate a number of games and periodically capture the agents' behavioural information from the gaming environment during each simulation. This information is stored in a series of status records that can be later analysed. We compare and contrast the behaviours of agents in the evolved teams to see if there is a correlation between a team's performance (fitness score) and the combined behaviours of the team's agents. This approach could also be applied to other GP-evolved teams in different domains.

@inproceedings{1277347, abstract = {This paper presents an approach to analyse the behaviours of teams of autonomous agents who work together to achieve a common goal. The agents in a team are evolved together using a genetic programming (GP) [8] approach where each team of agents is represented as a single GP tree or chromosome. A number of such teams are evolved and their behaviours analysed in an attempt to identify combinations of individual agent behaviours that constitute good (or bad) team behaviour. For each team we simulate a number of games and periodically capture the agents' behavioural information from the gaming environment during each simulation. This information is stored in a series of status records that can be later analysed. We compare and contrast the behaviours of agents in the evolved teams to see if there is a correlation between a team's performance (fitness score) and the combined behaviours of the team's agents. This approach could also be applied to other GP-evolved teams in different domains.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {London}, author = {Doherty, Darren and O'Riordan, Colm}, biburl = {https://www.bibsonomy.org/bibtex/220132dc6d0b273f54b6e2eb3f0fe85e6/brazovayeye}, booktitle = {GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation}, editor = {Thierens, Dirk and Beyer, Hans-Georg and Bongard, Josh and Branke, Jurgen and Clark, John Andrew and Cliff, Dave and Congdon, Clare Bates and Deb, Kalyanmoy and Doerr, Benjamin and Kovacs, Tim and Kumar, Sanjeev and Miller, Julian F. and Moore, Jason and Neumann, Frank and Pelikan, Martin and Poli, Riccardo and Sastry, Kumara and Stanley, Kenneth Owen and Stutzle, Thomas and Watson, Richard A and Wegener, Ingo}, interhash = {e32fc8a84ff52949234e9e8a31d842a8}, intrahash = {20132dc6d0b273f54b6e2eb3f0fe85e6}, isbn13 = {978-1-59593-697-4}, keywords = {AI, Applications, Real-World agents, algorithms, analysis, artificial behaviour cooperative genetic intelligence, phenotypic programming, tactical team}, month = {7-11 July}, notes = {GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071}, organisation = {ACM SIGEVO (formerly ISGEC)}, pages = {1951--1958}, publisher = {ACM Press}, publisher_address = {New York, NY, USA}, timestamp = {2008-06-19T17:38:48.000+0200}, title = {A phenotypic analysis of {GP}-evolved team behaviours}, url = {http://doi.acm.org/10.1145/1276958.1277347}, volume = 2, year = 2007 }