Abstract. Inspired by a method by Jones et al. (1993), we present a global optimization algorithm based on multilevel coordinate search. It is guaranteed to converge if the function is continuous in the neighborhood of a global minimizer. By starting a local search from certain good points, an improved convergence result is obtained. We discuss implementation details and give some numerical results.

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Despite the recent advances in distributed MDP frameworks for reasoning about multiagent teams, these frameworks mostly do not reason about resource constraints, a crucial issue in teams. To address this shortcoming, we provide four key contributions. First, we introduce EMTDP, a distributed MDP framework where agents must not only maximize expected team reward, but must simultaneously bound expected resource consumption. While there exist single-agent constrained MDP (CMDP) frameworks that reason about resource constraints, EMTDP is not just a CMDP with multiple agents. Instead, EMTDP must resolve the miscoordination that arises due to policy randomization. Thus, our second contribution is an algorithm for EMTDP transformation, so that resulting policies, even if randomized, avoid such miscoordination. Third, we prove equivalence of di#erent techniques of EMTDP transformation. Finally, we present solution algorithms for these EMTDPs and show through experiments their e#ciency in solving application-sized problems.

Decision-theoretic frameworks have been successfully applied to build agent/agent-teams acting in uncertain environments. Markovian models like the Markov Decision Problem (MDP), Partially Observable MDP (POMDP) and Decentralized POMDP (Dec-POMDP) provide efficient algorithms to find optimal policies for agent/agent-teams acting in accessible or inaccessible, stochastic environments with known transition model. However, such optimal policies are unable to deal with the challenge of security (ability to deal with intentional threats from other agents) in adversarial environments. Game-theoretic frameworks like stochastic games (SGs) and partially observable SGs (POSGs) find optimal secure policies assuming knowledge of action/reward structure of all actors (agent/agent-team and its adversaries) which is unrealistic in many situations. Real world domains exist where the agent/agent-team knows its transition and reward but has partial or no model of the adversaries. Given these problems with existing frameworks, in the present proposal I provide algorithms for secure optimal policies based on the MDP/Dec-POMDP frameworks with no adversary model. In such unmodeled-adversary domains, action randomization can effectively deteriorate an adversary’s capability to predict and exploit an agent/agent-team’s actions. Unfortunately,