Human behavior representation in military simulations is not sufficiently realistic, specially the decision making in synthetic military commanders.One of the problems in decision making is that the decisions are predictable. In order to address some of these deficiencies, we have developed a computer implementation of Recognition Primed Decision (RPD) making model using Soar cognitive architecture and it is referred to as RPD-Soar agent in this paper. The proposed implementation is evaluated using prototypical scenarios arising in command decision making in tactical situations. Due to the ability of the RPD-Soar agent to mentally simulate applicable courses of action it is possible for the agent to handle new situations very effectively using its prior knowledge. The variability in behavior within an agent is a desirable characteristic. Variability in agents may be produced through randomness but randomness also introduces undesirable behavior. The observed variability in the RPD-Soar agent is due to reasonable but some times sub-optimal choices given to the agent. RPD-Soar agent developed in this paper exhibits the ability to change decision making strategy with experience. And the preliminary results clearly demonstrate the ability of the model to represent human behavior variability within and across individuals.

This paper introduces the subnet generation problem (SGP) which is a new type of network routing problem that is found, for example, in some peer-to-peer applications. We explore two algorithms to solve the SGP by exploiting its special structure. The first algorithm, the tree search algorithm (TS), is an adaptation of an existing algorithm. TS decomposes the SGP into a master problem solved by a systematic tree search, and a subproblem solved by incomplete but efficient heuristics. Our question is what happens if we sacrifice the complete search in the master problem in exchange for better exploration of the search space.In order to answer this, we present a second algorithm, the local search algorithm (LS), which replaces the tree search with non-systematic search in the master problem. The two algorithms are compared in an experimental study.