Quantifying Emergent Behavior of Autonomous Robots
Quantifying behaviors of robots which were generated autonomously from task-independent objective functions is an important prerequisite for objective comparisons of algorithms and movements of animals. The temporal sequence of such a behavior can be considered as a time series and hence complexity...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-10-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/17/10/7266 |
| _version_ | 1811184823051485184 |
|---|---|
| author | Georg Martius Eckehard Olbrich |
| author_facet | Georg Martius Eckehard Olbrich |
| author_sort | Georg Martius |
| collection | DOAJ |
| description | Quantifying behaviors of robots which were generated autonomously from task-independent objective functions is an important prerequisite for objective comparisons of algorithms and movements of animals. The temporal sequence of such a behavior can be considered as a time series and hence complexity measures developed for time series are natural candidates for its quantification. The predictive information and the excess entropy are such complexity measures. They measure the amount of information the past contains about the future and thus quantify the nonrandom structure in the temporal sequence. However, when using these measures for systems with continuous states one has to deal with the fact that their values will depend on the resolution with which the systems states are observed. For deterministic systems both measures will diverge with increasing resolution. We therefore propose a new decomposition of the excess entropy in resolution dependent and resolution independent parts and discuss how they depend on the dimensionality of the dynamics, correlations and the noise level. For the practical estimation we propose to use estimates based on the correlation integral instead of the direct estimation of the mutual information based on next neighbor statistics because the latter allows less control of the scale dependencies. Using our algorithm we are able to show how autonomous learning generates behavior of increasing complexity with increasing learning duration. |
| first_indexed | 2024-04-11T13:20:02Z |
| format | Article |
| id | doaj.art-3c2601a4de4244b8be957caacdbcbb75 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T13:20:02Z |
| publishDate | 2015-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-3c2601a4de4244b8be957caacdbcbb752022-12-22T04:22:15ZengMDPI AGEntropy1099-43002015-10-0117107266729710.3390/e17107266e17107266Quantifying Emergent Behavior of Autonomous RobotsGeorg Martius0Eckehard Olbrich1IST Austria, Am Campus 1, 3400 Klosterneuburg, AustriaMax Planck Institute for Mathematics in the Sciences, Inselstr. 22, 04103 Leipzig, GermanyQuantifying behaviors of robots which were generated autonomously from task-independent objective functions is an important prerequisite for objective comparisons of algorithms and movements of animals. The temporal sequence of such a behavior can be considered as a time series and hence complexity measures developed for time series are natural candidates for its quantification. The predictive information and the excess entropy are such complexity measures. They measure the amount of information the past contains about the future and thus quantify the nonrandom structure in the temporal sequence. However, when using these measures for systems with continuous states one has to deal with the fact that their values will depend on the resolution with which the systems states are observed. For deterministic systems both measures will diverge with increasing resolution. We therefore propose a new decomposition of the excess entropy in resolution dependent and resolution independent parts and discuss how they depend on the dimensionality of the dynamics, correlations and the noise level. For the practical estimation we propose to use estimates based on the correlation integral instead of the direct estimation of the mutual information based on next neighbor statistics because the latter allows less control of the scale dependencies. Using our algorithm we are able to show how autonomous learning generates behavior of increasing complexity with increasing learning duration.http://www.mdpi.com/1099-4300/17/10/7266excess entropymutual informationpredictive informationquantificationautonomous robotsbehaviorcorrelation integral |
| spellingShingle | Georg Martius Eckehard Olbrich Quantifying Emergent Behavior of Autonomous Robots Entropy excess entropy mutual information predictive information quantification autonomous robots behavior correlation integral |
| title | Quantifying Emergent Behavior of Autonomous Robots |
| title_full | Quantifying Emergent Behavior of Autonomous Robots |
| title_fullStr | Quantifying Emergent Behavior of Autonomous Robots |
| title_full_unstemmed | Quantifying Emergent Behavior of Autonomous Robots |
| title_short | Quantifying Emergent Behavior of Autonomous Robots |
| title_sort | quantifying emergent behavior of autonomous robots |
| topic | excess entropy mutual information predictive information quantification autonomous robots behavior correlation integral |
| url | http://www.mdpi.com/1099-4300/17/10/7266 |
| work_keys_str_mv | AT georgmartius quantifyingemergentbehaviorofautonomousrobots AT eckehardolbrich quantifyingemergentbehaviorofautonomousrobots |