A theoretical model derives the universal mammalian vascular branching exponent α* ≈ 2.72 from a network-level minimax principle and topological rigidity theorem grounded in ATP costs, yielding α*_model ≈ 2.626 with heterogeneities shifting it to observed values.
In: Logics in Artificial Intelligence , pp
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In bandit-feedback zero-sum games, uncoupled algorithms achieve last-iterate Nash convergence at the optimal rate of O(T^{-1/4}).
Log-barrier regularization in online mirror descent attains the optimal Õ(t^{-1/4}) last-iterate convergence rate in uncoupled zero-sum matrix games under bandit feedback.
Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.
Sublevel sets of invex functions are connected under mild assumptions, with the result extended to solution sets in invex-incave minimax problems and incave games.
Explores value-level and graph-level aggregation in multi-agent value-based argumentation and proposes aggregating rankings extracted from attack relations as a third method.
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Structure from Strategic Interaction & Uncertainty: Risk Sensitive Games for Robust Preference Learning
Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.