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You searched for subject:(Animat). Showing records 1 – 4 of 4 total matches.

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1. Strandman, Anton. Exploration Strategies for Homeostatic Agents: Continuous and dynamic exploration for homeostatic regulation using deep reinforcement learning .

Degree: Chalmers tekniska högskola / Institutionen för data och informationsvetenskap, 2019, Chalmers University of Technology

 This paper introduces and evaluates four novel exploration strategies for homeostatic agents. Homeostatic agents have the objective of keeping some internal variables as close to… (more)

Subjects/Keywords: artificial general intelligence; multi-objective reinforcement learning; exploration; homeostatic regulation; animat; homeostatic exploration

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APA (6th Edition):

Strandman, A. (2019). Exploration Strategies for Homeostatic Agents: Continuous and dynamic exploration for homeostatic regulation using deep reinforcement learning . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/300061

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Strandman, Anton. “Exploration Strategies for Homeostatic Agents: Continuous and dynamic exploration for homeostatic regulation using deep reinforcement learning .” 2019. Thesis, Chalmers University of Technology. Accessed August 09, 2020. http://hdl.handle.net/20.500.12380/300061.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Strandman, Anton. “Exploration Strategies for Homeostatic Agents: Continuous and dynamic exploration for homeostatic regulation using deep reinforcement learning .” 2019. Web. 09 Aug 2020.

Vancouver:

Strandman A. Exploration Strategies for Homeostatic Agents: Continuous and dynamic exploration for homeostatic regulation using deep reinforcement learning . [Internet] [Thesis]. Chalmers University of Technology; 2019. [cited 2020 Aug 09]. Available from: http://hdl.handle.net/20.500.12380/300061.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Strandman A. Exploration Strategies for Homeostatic Agents: Continuous and dynamic exploration for homeostatic regulation using deep reinforcement learning . [Thesis]. Chalmers University of Technology; 2019. Available from: http://hdl.handle.net/20.500.12380/300061

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Tech

2. Gaff, Douglas G. Architecture design and simulation for distributed learning classifier systems.

Degree: MS, Electrical Engineering, 1995, Virginia Tech

  In this thesis, we introduce the Distributed Learning Classifier System (DLCS) as a novel extension of J. H. Holland's standard learning classifier system. While… (more)

Subjects/Keywords: distributed artificial intelligence; robotics; network communications; the animat problem; LD5655.V855 1995.G344

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APA (6th Edition):

Gaff, D. G. (1995). Architecture design and simulation for distributed learning classifier systems. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/41120

Chicago Manual of Style (16th Edition):

Gaff, Douglas G. “Architecture design and simulation for distributed learning classifier systems.” 1995. Masters Thesis, Virginia Tech. Accessed August 09, 2020. http://hdl.handle.net/10919/41120.

MLA Handbook (7th Edition):

Gaff, Douglas G. “Architecture design and simulation for distributed learning classifier systems.” 1995. Web. 09 Aug 2020.

Vancouver:

Gaff DG. Architecture design and simulation for distributed learning classifier systems. [Internet] [Masters thesis]. Virginia Tech; 1995. [cited 2020 Aug 09]. Available from: http://hdl.handle.net/10919/41120.

Council of Science Editors:

Gaff DG. Architecture design and simulation for distributed learning classifier systems. [Masters Thesis]. Virginia Tech; 1995. Available from: http://hdl.handle.net/10919/41120


Robert Gordon University

3. McMinn, David. Using evolutionary artificial neural networks to design hierarchical animat nervous systems.

Degree: PhD, 2001, Robert Gordon University

 The research presented in this thesis examines the area of control systems for robots or animats (animal-like robots). Existing systems have problems in that they… (more)

Subjects/Keywords: 003.5; Artificial neural networks; Control systems; Animat; Artificial nervous system

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

McMinn, D. (2001). Using evolutionary artificial neural networks to design hierarchical animat nervous systems. (Doctoral Dissertation). Robert Gordon University. Retrieved from http://hdl.handle.net/10059/427

Chicago Manual of Style (16th Edition):

McMinn, David. “Using evolutionary artificial neural networks to design hierarchical animat nervous systems.” 2001. Doctoral Dissertation, Robert Gordon University. Accessed August 09, 2020. http://hdl.handle.net/10059/427.

MLA Handbook (7th Edition):

McMinn, David. “Using evolutionary artificial neural networks to design hierarchical animat nervous systems.” 2001. Web. 09 Aug 2020.

Vancouver:

McMinn D. Using evolutionary artificial neural networks to design hierarchical animat nervous systems. [Internet] [Doctoral dissertation]. Robert Gordon University; 2001. [cited 2020 Aug 09]. Available from: http://hdl.handle.net/10059/427.

Council of Science Editors:

McMinn D. Using evolutionary artificial neural networks to design hierarchical animat nervous systems. [Doctoral Dissertation]. Robert Gordon University; 2001. Available from: http://hdl.handle.net/10059/427

4. Abdelmotaleb, Ahmed Mostafa Othman. Evolution of spiking neural networks for temporal pattern recognition and animat control.

Degree: PhD, 2016, University of Hertfordshire

 I extended an artificial life platform called GReaNs (the name stands for Gene Regulatory evolving artificial Networks) to explore the evolutionary abilities of biologically inspired… (more)

Subjects/Keywords: 006.3; Evolving Spiking Neural Networks; Temporal Pattern Recognition; Animat Foraging; Genetic Algorithm; Gene regulatory networks; Leaky integrate and fire; Spinnaker; Evolutionary algorithm

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Abdelmotaleb, A. M. O. (2016). Evolution of spiking neural networks for temporal pattern recognition and animat control. (Doctoral Dissertation). University of Hertfordshire. Retrieved from http://hdl.handle.net/2299/17181

Chicago Manual of Style (16th Edition):

Abdelmotaleb, Ahmed Mostafa Othman. “Evolution of spiking neural networks for temporal pattern recognition and animat control.” 2016. Doctoral Dissertation, University of Hertfordshire. Accessed August 09, 2020. http://hdl.handle.net/2299/17181.

MLA Handbook (7th Edition):

Abdelmotaleb, Ahmed Mostafa Othman. “Evolution of spiking neural networks for temporal pattern recognition and animat control.” 2016. Web. 09 Aug 2020.

Vancouver:

Abdelmotaleb AMO. Evolution of spiking neural networks for temporal pattern recognition and animat control. [Internet] [Doctoral dissertation]. University of Hertfordshire; 2016. [cited 2020 Aug 09]. Available from: http://hdl.handle.net/2299/17181.

Council of Science Editors:

Abdelmotaleb AMO. Evolution of spiking neural networks for temporal pattern recognition and animat control. [Doctoral Dissertation]. University of Hertfordshire; 2016. Available from: http://hdl.handle.net/2299/17181

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