Applying Short-term Memory to Social Search Agents
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This paper presents about our research in social search. Generally, the research in social search falls into two principal
challenges. The first challenge is how to find more relevant answers to the question. The second one is how to increase speed in
finding relevant answers.
Recently, we had provided two algorithms called Asknext and Question Waves to find more relevant answers compared to the
baseline algorithm BFS. But, the search speed of the two proposed algorithms still the subject to improve.
In this paper, we introduce the agents’ ability of learning the answers from the interactions with other agents so that they can
quickly answer the question of other agents. We model this learning process by implementing the concept of data caching as the
short-term memory of each social search agent. The result improvement of the speediness and the reduction of the number of
messages used to communicate between agents, after apply agent's short-term memory concept, demonstrates the usefulness of
the proposed approach