Merge pull request #1384 from Axelrod-Python/marcharper-patch-1

Update citations.md

Author: Nikoleta-v3

axelrod/fingerprint.py

@@ -441,7 +441,7 @@ def fingerprint(
         filename: str = None,
         progress_bar: bool = True,
         seed: int = None,
-    ) -> np.array:
+    ) -> np.ndarray:
         """Creates a spatial tournament to run the necessary matches to obtain
         fingerprint data.
 

axelrod/strategies/ann.py

@@ -22,7 +22,7 @@ def num_weights(num_features, num_hidden):
     return size
 
 
-def compute_features(player: Player, opponent: Player) -> List[int]:
+def compute_features(player: Player, opponent: Player) -> np.ndarray:
     """
     Compute history features for Neural Network:
     * Opponent's first move is C
@@ -91,38 +91,39 @@ def compute_features(player: Player, opponent: Player) -> List[int]:
     total_player_c = player.cooperations
     total_player_d = player.defections
 
-    return [
-        opponent_first_c,
-        opponent_first_d,
-        opponent_second_c,
-        opponent_second_d,
-        my_previous_c,
-        my_previous_d,
-        my_previous2_c,
-        my_previous2_d,
-        opponent_previous_c,
-        opponent_previous_d,
-        opponent_previous2_c,
-        opponent_previous2_d,
-        total_opponent_c,
-        total_opponent_d,
-        total_player_c,
-        total_player_d,
-        len(player.history),
-    ]
+    return np.array(
+        (
+            opponent_first_c,
+            opponent_first_d,
+            opponent_second_c,
+            opponent_second_d,
+            my_previous_c,
+            my_previous_d,
+            my_previous2_c,
+            my_previous2_d,
+            opponent_previous_c,
+            opponent_previous_d,
+            opponent_previous2_c,
+            opponent_previous2_d,
+            total_opponent_c,
+            total_opponent_d,
+            total_player_c,
+            total_player_d,
+            len(player.history),
+        )
+    )
 
 
 def activate(
     bias: List[float],
     hidden: List[float],
     output: List[float],
-    inputs: List[int],
+    inputs: np.ndarray,
 ) -> float:
     """
     Compute the output of the neural network:
         output = relu(inputs * hidden_weights + bias) * output_weights
     """
-    inputs = np.array(inputs)
     hidden_values = bias + np.dot(hidden, inputs)
     hidden_values = relu(hidden_values)
     output_value = np.dot(hidden_values, output)

citations.md

@@ -1,7 +1,12 @@
 # Citations
 
+### 2021
+
+* Glynatsi, Nikoleta E., and Vincent A. Knight. "A bibliometric study of research topics, collaboration, and centrality in the iterated prisoner’s dilemma." Humanities and Social Sciences Communications 8.1 (2021): 1-12. [Nature.com](https://www.nature.com/articles/s41599-021-00718-9) [ArXiv](https://arxiv.org/abs/1911.06128)
+
 ### 2020
 
+* Kies, Martin. "Finding Best Answers for the Iterated Prisoner’s Dilemma Using Improved Q-Learning." [Available at SSRN 3556714](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3556714) (2020).
 * Krapohl, Sebastian, Václav Ocelík, and Dawid M. Walentek. "The instability of globalization: applying evolutionary game theory to global trade cooperation." [Public Choice](https://ideas.repec.org/a/kap/pubcho/vyid10.1007_s11127-020-00799-1.html) (2020): 1-21.
 * Glynatsi, Nikoleta E., and Vincent A. Knight. "A meta analysis of tournaments and an evaluation of performance in the Iterated Prisoner's Dilemma" arXiv preprint arXiv:2001.05911 (2019). [ArXiv](https://arxiv.org/abs/2001.05911)
 * Wang, Shiheng, and Fangzhen Lin. "Nice Invincible Strategy for the Average-Payoff IPD." [Proceedings of the AAAI Conference on Artificial Intelligence](https://aaai.org/ojs/index.php/AAAI/article/view/5604)
@@ -10,8 +15,8 @@
 
 ### 2019
 
-* T.J. Gaffney, Marc Harper, Vincent A. Knight. "Memory depth of finite state machine strategies for the iterated prisoner's dilemm" arXiv preprint arxiv:1912.04493 (2019). [ArXiv](https://arxiv.org/abs/1912.04493)
-* Glynatsi, Nikoleta E., and Vincent A. Knight. "A bibliometric study of research topics, collaboration and influence in the field of the Iterated Prisoner's Dilemma" arXiv preprint arXiv:1911.06128 (2019). [ArXiv](https://arxiv.org/abs/1911.06128)
+* Thomas, Robert W., and José M. Vidal. "Ad Hoc Vehicle Platoon Formation." [2019 SoutheastCon. IEEE](Emergence and Stability of Self-Evolved Cooperative Strategies using Stochastic Machines) (2019)
+* T.J. Gaffney, Marc Harper, Vincent A. Knight. "Memory depth of finite state machine strategies for the iterated prisoner's dilemma" arXiv preprint arxiv:1912.04493 (2019). [ArXiv](https://arxiv.org/abs/1912.04493)
 * O'Neil, Daniel A., and Mikel D. Petty. "Synthesizing Social Networks with Iterated Prisoners' Dilemma." [Proceedings of the International Conference on Modeling, Simulation and Visualization Methods (MSV).](https://csce.ucmss.com/cr/books/2019/LFS/CSREA2019/MSV2345.pdf) The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2019.
 * Collins, David. "A Genetic Algorithm Approach to the Iterated Prisoner’s Dilemma." [Undergraduate Research in Natural and Clinical Science and Technology Journal](https://urncst.com/index.php/urncst/article/view/155) (2019): 1-9.
 * Wang, Shiheng, and Fangzhen Lin. "Invincible Strategies of Iterated Prisoner's Dilemma." Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 2019.  [ArXiv](https://arxiv.org/abs/1712.06488)
@@ -24,12 +29,11 @@
 * Latorre, Julian F. and Ospina, Juan Pablo and Ortiz, Jorge E. "A Coalitional Game for Achieving Emergent Cooperation in Ad Hoc Networks Through Sympathy and Commitment." Springer (2018). [Springer](https://link.springer.com/chapter/10.1007/978-3-030-00350-0_30)
 * Moura, Jose, and David Hutchison. "Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends." IEEE Communications Surveys & Tutorials (2018). [IEEE CS&T](https://ieeexplore.ieee.org/abstract/document/8424815) [ArXiv](https://arxiv.org/abs/1704.00323)
 * Neumann, Shai and Sood, Suraj and Hollander, Markus et al. "Using Bots in Strategizing Group Compositions to Improve Decision--Making Processes" Springer (2018). [Springer](https://link.springer.com/chapter/10.1007/978-3-319-91467-1_24)
-* Nicolas, Anastassacos and Mirco, Musolesi. "Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas."arXiv preprint arXiv:1809.10007 (2018). [ArXiv](https://arxiv.org/abs/1809.10007)
+* Nicolas, Anastassacos and Mirco, Musolesi. "Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas." arXiv preprint arXiv:1809.10007 (2018). [ArXiv](https://arxiv.org/abs/1809.10007)
 * Skiba, Grażyna, et al. "Flexible asynchronous simulation of iterated prisoner’s dilemma based on actor model." Simulation Modelling Practice and Theory 83 (2018): 75-92.
 
 ### 2017
 
-* Hayes, Victoria. The Evolution of Cooperation: A Recreation of Axelrod's Computer Tournament. The University of North Carolina at Greensboro, (2017). [PDF](http://libres.uncg.edu/ir/uncg/f/Hayes_uncg_0154M_12243.pdf)
 * Harper, Marc, et al. "Reinforcement learning produces dominant strategies for the Iterated Prisoner’s Dilemma." PloS one 12.12 (2017): e0188046.  [PLOS](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188046)
 
 ### 2016
@@ -40,8 +44,10 @@
 
 * Knight, Vincent. "Playing Games: A Case Study in Active Learning Applied to Game Theory." [MSOR Connections 14.1 (2015): 28-38](https://journals.gre.ac.uk/index.php/msor/article/download/254/254).
 
-### Other Preprints and Disserations
+### Disserations
 
-* Thomas, Robert W., and José M. Vidal. "Ad Hoc Vehicle Platoon Formation." (2019) [Preprint](http://jmvidal.cse.sc.edu/papers/thomas19a.pdf)
+* Glynatsi, Nikoleta. "Understanding responses to environments for the Prisoner's Dilemma: A meta analysis, multidimensional optimisation and machine learning approach". PhD Thesis, Cardiff University. (2020) [Available Online](http://orca.cf.ac.uk/135221/).
+* Santana, Jonas Cardoso Carvalho. "Aprendizado de máquina e o dilema dos prisioneiros". (2020)
 * Black, Ben. "An Empirical Evaluation of the Ohtsuki-Nowak Approximation." [Master's Thesis](https://www.lancaster.ac.uk/~blackb/documents/MScDiss.pdf). (2018).
-* Zakirov, Bahti. "Modes of Information Flow." (2017) [Preprint](http://london.ucdavis.edu/~reu/REU17/Papers/zakirov.pdf)
+* Hayes, Victoria. The Evolution of Cooperation: A Recreation of Axelrod's Computer Tournament. The University of North Carolina at Greensboro, (2017). [PDF](http://libres.uncg.edu/ir/uncg/f/Hayes_uncg_0154M_12243.pdf)
+* Latorre Ochoa, Julian Felipe. "Compromiso y simpatía: emotividad en la toma de decisiones cooperativas en redes estocásticas de siguiente generación". [Available online](https://repositorio.unal.edu.co/handle/unal/60956) (2017)