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Two paradigms currently exist for information search. The first is the library paradigm, which has been largely automated and is the prevailing paradigm in today’s web search. The second is the village paradigm, and although it is older than the library paradigm, its automation has not been considered, yet certain elements of its key aspects have been automated, as in the cases of the Q&A communities or novel services such as Quora. The increasing popularity and availability of online social networks and question-answering communities have encouraged revisiting of the automation of the village paradigm owing to new helpful developments, primarily that people are more connected with their acquaintances on the internet and their contact lists are available. In this survey, we study how the village paradigm is today partially automated: we consider the selection of candidates for answering questions, answering questions automatically and helping candidates to decide what questions to answer. Other aspects are also considered, for example, the automation of a reward system. We conclude that a next step towards the automation of the village paradigm involves intelligent agents that can leverage a P2P (peer-to-peer) social network, which will create new and interesting issues deeply entwined with social networks in the form of information processing by agents in parallel and side by side with peopleAlbert Trias acknowledges the PhD support grant Universitat de Girona BR/10. This work was partially funded by the MINECO projects IPT20120482430000 (MIDPOINT), Nuevos enfoques de preservacion digital con mejor gestion de costes que garantizan su sostenibilidad and IPT-430000-2010-13, Social powered Agents for Knowledge search Engine (SAKE), the EU DURAFILE no. 605356, FP7-SME-2013, BSG-SME (Research for SMEs) Innovative Digital Preservation using Social Search in Agent Environments, and the AGAUR grant for the CSI-ref.2009SGR-120

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 approachThis 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 146