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

This work was supported in part by by the EU's 7FP under grant agreement no 316097, by the TIN2013-48040-R (QWAVES) Nuevos métodos de automatización de la búsqueda social basados en waves de preguntas, the IPT20120482430000 (MIDPOINT) Nuevos enfoques de preservación digital con mejor gestión de costes que garantizan su sostenibilidad, VirCoin2SME – num. H2020-MSCA-RISE SEP 210165853. Social, complementary or community virtual currencies transfer of knowledge to SME: a new era for competitiveness and entrepreneurship, and VISUAL AD, RTC-2014-2566-7 and GEPID, RTC-2014-2576-7, as well as the grup de recerca consolidat CSI-ref. 2014 SGR 1469