One well known and long-lasting problem in the video tracking is that one particular algorithm would perform well on a certain environmental characteristic. Whenever the characteristic in the scene changes; the performance of the algorithm affected. This research proposes a multiagent-based for video tracking system. The agents follow the odd-man out strategy; which odd agents will be credited less than the favorite ones. We tested our algorithm against two tough videos. The results show that our approach yield satisfactory outcomes. The final tracking results are always within the boundary of the groundtruth; given that there are two out of five correct results.

Shapley value is regarded as a fair payoff distribution concept for cooperative agents. While traditional cooperative game assume superadditivity and non-externalty; real world environments do not hold this assumption. We show that in linear production game; the environment is non-superadditive is with externalties. In such environment; grand coalition does not provide optimal solution to the system. Consequently; applying traditional shapley value does not provide an attractive payoff to agents. In addition; fairness may also be lost because individual payoffs are less than singleton coalition values. We show how this environments may occur and how we can propose a more attractive and; still; fair payoffs to agents.

We present a multiagent-based framework for crowd simulation in large space urban area on a standalone PC. We use Belief-Desire-Intention (BDI) for modeling individual agent behavior. We use RVO for handling a large number of agents. The simulation engine is Unity3d which also take care of the visualization. We experimented our framework with up to 20;000 agents; navigating them from origins to destinations. We found that we can navigate agents successfully. The execution time increases when the number of agent increase. The visualization becomes slow when the number of agent is higher than 1000 agents. We found that the the simulation steps also increases when the number of agent is not higher than 5005.