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Mohammad Ebrahim Shiri

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EUMAS Conference 2015 Conference Paper

Trust-Based Multiagent Credit Assignment (TMCA)

Samira Nazari
Mohammad Ebrahim Shiri

Abstract In Multiagent Reinforcement Learning (MARL), a single scalar reinforcement signal is the sole reliable feedback that members of a team of learning agents can receive from the environment around them. Hence, the distribution of the environmental feedback signal among learning agents, also known as the “ Multiagent Credit Assignment ” (MCA), is among the most challenging problems in MARL. In this paper, the authors propose an extended solution to the problem of MCA. In the proposed method, called “ Trust-based Multiagent Credit Assignment ” (TMCA), a trust and reputation based model is utilized to evaluate the trustworthiness of the learning agents. Unlike the existing methods, TMCA not only qualifies to benefit from the knowledge and expertise of the sole target agent (the agent for which the credit is being evaluated), but also from the knowledge and expertise of the whole as a team. To evaluate this method, the effect of different task types (e. g. AND vs. OR) are studied. Our simulations show the superiority of the proposed method in comparison to the prior investigated methods even in noisy environments, despite a reduction (caused by the noise) in the performance.

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EAAI Journal 2014 Journal Article

Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems

Nafiseh Imanian
Mohammad Ebrahim Shiri
Parham Moradi

Artificial bee colony (ABC) is a swarm optimization algorithm which has been shown to be more effective than the other population based algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). Since it was invented, it has received significant interest from researchers studying in different fields because of having fewer control parameters, high global search ability and ease of implementation. Although ABC is good at exploration, the main drawback is its poor exploitation which results in an issue on convergence speed in some cases. Inspired by particle swarm optimization, we propose a modified ABC algorithm called VABC, to overcome this insufficiency by applying a new search equation in the onlooker phase, which uses the PSO search strategy to guide the search for candidate solutions. The experimental results tested on numerical benchmark functions show that the VABC has good performance compared with PSO and ABC. Moreover, the performance of the proposed algorithm is also compared with those of state-of-the-art hybrid methods and the results demonstrate that the proposed method has a higher convergence speed and better search ability for almost all functions.

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