Dynamic Noncooperative Game Theory
By Tamer Basar, G. Olsder. 1982
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Sergey Levine
- Yoshua Bengio
- Abhinav Kumar Gupta
- Pieter Abbeel
- Dacheng Tao
- Ruslan Salakhutdinov
- Bo Li
- Trevor Darrell
Citations in the corpus
- Price of Transparency in Strategic Machine Learning (2016, methodology): “We use standard game theoretic notations for the related results throughout this paper (cf. [7]).”
- Supervision via competition: Robot adversaries for learning tasks (2016, background): “cs or reinforcement learning has trained an adversary to guide exploration or for sample efficiency. Game Theory. We formulate our adversarial training as a two player zero sum repeated game (refer to [26] for an overview of game theory). Game theory has been explored in many robotics applications. M”
- Social behavior for autonomous vehicles (2019, unknown): “4 formulated for all m agents simultaneously defines a dynamic game (14).”
- Reputation-based framework for high integrity sensor networks (2004, background): “There is a branch of game theory, non-cooperative game theory [Basar and Olsder 1999], which deals with exactly the same scenario of non-rational participating entities.”
- Nash Equilibria of Static Prediction Games (2009, background): “3 in [6], at least one Nash equilibrium exists.”
- Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach (2013, background): “This leads to imperfect competition, where each firm determines its unit price based on its available power.”
- Energy Sharing Management for Microgrids With PV Prosumers: A Stackelberg Game Approach (2017, background): “The leader makes decision from the perspective of his own purpose while followers make decisions according to the decision taken by the leader [24].”
- On selfish behavior in CSMA/CA networks (2005, methodology): “For this purpose, we make use of the theory of dynamic and repeated games [ 22 ], [16].”
- Randomized Kinodynamic Planning (1999, background): “the nonholonomic metric (Laumond, Sekhavat, and Lamiraux 1998), the value function (Sundar and Shiller 1997), and the cost-to-go function (Başar and Olsder 1982; LaValle 1995).”
- Computing optimal randomized resource allocations for massive security games (2009, methodology): “A strong Stackelberg equilibrium exists in all Stackelberg games, but a weak Stackelberg equilibrium may not [2].”