[Where I stopped reading so carefully, covers game theory]

- (360) Treats stochasticity as a multi-player game
- (361) As in motion planning, an optimal decision may not exist because of open sets
- (369) Minimax solution has best worst case bounds
- Stochasticity is separated into nondeterminism and probabalistic stochasticity, adversarial vs nonadversarial
- (373) Optimal decision making in terms of classification
- (377) Param estimation

- (381) When using randomized strategies, the values for both players’ optimal actions can be equal in any 0-sum game, isn’t generally true of randomized policies.
- (382) Not all zero sum games have an equilibrium when deterministic strategies are used
- (384) Finding an equilibria for randomized policies can be done utilizing the following:
- Security strategy for ea player can be found by only looking at deterministic strategies for other player
- If strategy for other player is fixed expected cost is a linear f() of action prbs

- (390) Computing Nash equilibria is harder than the above because of finding saddle points (?)
- (399) “The frequentist approach attempts to be more conservative and rigorous, with the result being that weaker statements are made regarding decisions.”
- (400) MaxEnt
- (402) Argument against worst-case analysis

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