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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zahadat, Payam Hamann, Heiko Schmickl, Thomas |
| Abstract | There are multiple different ways of implementing artificial evolution of collective behaviors. Besides a classical offline evolution approach, there is, for example, the option of environment-driven distributed evolutionary adaptation in the form of an artificial ecology [2] and more generally there is the approach of embodied evolution [1,3,6]. Another recently reported approach is the application of novelty search to swarm robotics [5]. In the following, we report an extension of the approach of [7]. The underlying concept is an information-theoretic analogon to thermodynamic (Helmholtz) free energy [8]. The assumption is that the brain is permanently trying to predict future perceptions and that minimizing the prediction error is basically inherent to brains. This is defined by the 'free-energy principle' of [4]. The struggle for prediction success requires a complementary force that represents curiosity and exploration. In this abstract we present an extended method called diverse-prediction that rewards not only for correct predictions but also for each visited sensory state. This proves to be a better approach compared to the method prediction that was reported before[7]. |
| Starting Page | 1245 |
| Ending Page | 1246 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781450334884 |
| DOI | 10.1145/2739482.2768492 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-07-11 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Diversity Evolutionary adaptation Prediction Collective behaviors |
| Content Type | Text |
| Resource Type | Article |
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