Abstract
In a distributed game we imagine a team Player engaging a team Opponent in a distributed fashion. No longer can we assume that moves of Player and Opponent alternate. Rather the history of a play more naturally takes the form of a partial order of dependency between occurrences of moves. How are we to define strategies within such a game, and how are we to adjoin probability to such a broad class of strategies? The answer yields a surprisingly rich language of probabilistic distributed strategies and the possibility of programming (optimal) probabilistic strategies. Along the way we shall encounter solutions to: the need to mix probability and nondeterminism; the problem of parallel causes in which members of the same team can race to make the same move, and why this leads us to invent a new model for the semantics of distributed systems.
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Notes
- 1.
We consider two strategies \(\sigma :S\rightarrow A\) and \(\sigma ':S'\rightarrow A\) to be essentially the same if there is an isomorphism \(f:S\cong S'\) of event structures respecting polarity such that \(\sigma = \sigma 'f\).
- 2.
A Scott-open subset of configurations is upwards-closed w.r.t. inclusion and such that if it contains the union of a directed subset S of configurations then it contains an element of S. A continuous valuation is a function w from the Scott-open subsets of \(\,\!\mathcal{C}^\infty (E)\) to [0, 1] which is (normalized) \(w(\,\!\mathcal{C}^\infty (E)) = 1\); (strict) \(w(\emptyset ) = 0 \); (monotone) \(U \subseteq V \implies w(U)\le w(V)\); (modular) \(w(U \cup V) + w(U\cap V) = w(U) + w(V)\); and
(continuous) \(w(\bigcup _{i\in I} U_i) = \mathrm{sup}_{i\in I} w(U_i)\), for directed unions. The idea: w(U) is the probability of a result in open set U.
- 3.
Samy Abbes has pointed out that the same “drop condition” appears in early work of the Russian mathematician V.A.Rohlin [14](as relation (6) of Sect. 3, p.7). Its rediscovery in the context of event structures was motivated by the need to tie probability to the occurrences of events; it is sufficient to check the ‘drop condition’ for , in which the configurations \(x_i\) extend y with a single event.
- 4.
The use of “schedulers to resolve the probability or nondeterminism” in earlier work is subsumed by that of probabilistic and deterministic counter-strategies. Deterministic strategies coincide with those with assignment one to each finite configuration.
- 5.
Their treatment in [15] is slapdash.
- 6.
One way to define a rigid embedding is as a rigid map whose function is injective and reflects consistency.
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Acknowledgements
Thanks to Samy Abbes, Nathan Bowler, Simon Castellan, Pierre Clairambault, Marcelo Fiore, Mai Gehrke, Julian Gutierrez, Jonathan Hayman, Martin Hyland, Marc Lasson, Silvain Rideau, Daniele Varacca and Marc de Visme for helpful discussions. The concluding section is based on recent joint work with Marc de Visme while on his internship from ENS Paris. The support of Advanced Grant ECSYM of the European Research Council is acknowledged with gratitude.
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Winskel, G. (2015). On Probabilistic Distributed Strategies. In: Leucker, M., Rueda, C., Valencia, F. (eds) Theoretical Aspects of Computing - ICTAC 2015. ICTAC 2015. Lecture Notes in Computer Science(), vol 9399. Springer, Cham. https://doi.org/10.1007/978-3-319-25150-9_6
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