A Computational Model of Social-Learning Mechanisms

By Manuel Lopes, Francisco S. Melo, Ben Kenward, José Santos-Victor. 2009

28 citations recorded by the source corpus.

Subfields: Cultural Studies, Developmental and Educational Psychology, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cultural Studies

1. Adrien Baranes
2. Pierre-Yves Oudeyer
3. Sao Mai Nguyen
4. Manuel Lopes
5. Frederic Kaplan

In Developmental and Educational Psychology

1. Sao Mai Nguyen
2. Pierre-Yves Oudeyer
3. Adrien Baranes
4. Manuel Lopes
5. Francisco S. Melo
6. Frederic Kaplan
7. Clément Moulin-Frier
8. Jacqueline Gottlieb

In Sociology and Political Science

1. Pierre-Yves Oudeyer
2. Sao Mai Nguyen
3. Francisco S. Melo
4. Adrien Baranes

Citations in the corpus

• Active learning of inverse models with intrinsically motivated goal exploration in robots (2013, neutral): “…many goals/tasks in a parameterized task space) before focusing on different ways to perform the same tasks (here this means finding several alternative actions to achieve the same goal), knowledge-based exploration techniques like RIAC cannot be efficient in robots with redundant forward models.”
• Bootstrapping intrinsically motivated learning with human demonstration (2011, background): “others or by listening to their advice. Studies in robot learning by imitation and demonstration have already developed statistical inference mechanisms allowing the inference of new task constraints [4], [5], [7]. These techniques could be reused by intrinsically motivated learning architectures to”
• Socially guided intrinsic motivation for robot learning of motor skills (2013, methodology): “…pertains to the emulation level of social learning, where the observer witnesses someone produce a result on an object, but then employs his own policy repertoire to reproduce the result, as formalised in Lopes et al. (2009a), Call and Carpenter (2002), Whiten (2000), Nehaniv and Dautenhahn (2007).”
• Active Learning for Reward Estimation in Inverse Reinforcement Learning (2009, background): “One interesting aspect of this approach is that it can accommodate for differences between the demonstrator and the learner [1].”
• Bootstrapping Intrinsically Motivated Learning with Human Demonstrations (unknown): “Studies in robot learning by imitation and demonstration have already developed statistical inference mechanisms allowing the inference of new task constraints [4], [5], [7].”