Browsing by Author "Sombattheera, Chattrakul"

Now showing 1 - 5 of 5
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    A crowd simulation in large space urban
    Sudkhot, Panich; Sombattheera, Chattrakul (IEEE, 2018)
    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.
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    A multi agent-based video tracking algorithm
    Sombattheera, Chattrakul (IEEE, 2018)
    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.
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    An anytime algorithm for scheduling tasks for multiagent systems
    Sombattheera, Chattrakul; Phon-Amnuaisuk, Somnuk; Ang, Swee-Peng; Lee, Soo-Young (Springer International Publishing, 2017)
    This research proposes an any time algorithm for a task scheduling problem among agents. The tasks are composed of atomic tasks and are to be distributed to coalitions of agents as subtasks for parallel execution. We model the problem and propose an algorithm for it. The algorithm calls other low level algorithms to recursively generate plans for agents. The results show satisfactory results that the convergent times are reasonably short and are close to termination time in many settings. We also found that the distribution of input values affect the performance of the algorithm similar to the optimal coalition structure problem.
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    Fair payoff distribution in multi-agent systems under Pareto optimality
    Khamket, Thananchai; Sombattheera, Chattrakul (IEEE, 2017)
    This research proposes a set of algorithms to compute fair payoff distribution among agents in service composition domain based on their contribution. In our system; intelligent agents; representing service providers; negotiate among themselves and form composite services to satisfy multiple-objective requirements. The quality of service for each objective is measured in term of degree of satisfaction. The overall quality of service is achieved by maximizing requesters satisfaction on all objectives according to Pareto optimality. We then deploy Shapley Value concept for fair payoff distribution among agents based on their contributions to the requesters optimal satisfaction. Since the computational complexity for Shapley Value is exponential; we are interested in investigating how well the algorithms for computing payoff perform. We found that on a typical computer; the algorithm can cope with around 20 agents with reasonable computational time.
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    Fair payoffs distribution in linear production game by shapley value
    Intara, Benjawan; Sombattheera, Chattrakul (IEEE, 2018)
    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.