###### Abstract
This paper surveys current trends in museum AI chatbots and conversational AI platforms in general, motivated by the real need to introduce and utilize a new architecture that will deliver a museum conversational AI system capable of conducting human-like conversations, utilizing knowledge without limits. This need has been identified in the context of a research and development project for the Nikos Kazantzakis Museum in Greece. The proposed architecture aims to answer the abovementioned key questions by focusing to the design of a system that utilizes Knowledge Graphs (KG), has strong conversational skills by exploiting both NLU and ML techniques, and rely on the distributed and collaborative aspects of AI. A KG mainly describes real-world entities and their interrelations, organized in a large, directed graph (both a schema and the corresponded data are represented in the graph, lately with the widely used and well-known RDF model, in the form of triple-like statements). The integration of KGs to conversational AI (usually called hybrid AI) provides better rational dialogues that could eventually lead to more meaningful AI chatbot applications [14; 15]. In Figure 1 an exemplary KG about the Greek writer [PERSON] and related entities is depicted. In our early research [16] we have demonstrated preliminary results related to such an approach. Specifically, we introduced a single-chatbot system architecture that aims to retrieve knowledge from various KGs. A preliminary evaluation of a proof-of-concept system concerning the flow of the information and the interaction between the chatbot, and the KGs has been presented. In the current paper, we built upon the single-chatbot architecture, and we extend it by placing it within a novel approach of a knowledge graph-based distributed and collaborative system architecture, where several distributed AI chatbots, \"carrying\" their own distributed knowledge (encoded in their own KG and partially in the KGs of others), can \"work\" individually or in collaboration with others (in teams) at the same time. Such an approach aims to design museum chatbots that can provide robust and potentially unlimited knowledge to their visitors about the exhibits and the museum thematics, or even about knowledge that is far from being pre-engineered in the museum's knowledge base (e.g., retrieved from the Semantic Web). In such a multi-chatbot approach, the collaborating chatbots do not rely on a single central knowledge repository. They work in a distributed manner, utilizing different KGs (e.g., for different museum exhibitions and departments), providing this way more accurate and immediate answers to visitors' questions. In addition, these chatbots can collaborate and help each other by sharing knowledge and experiences that others do not have at the time of its initial acquisition. The contribution per section and the structure of this survey paper is as follows: Section 2 presents the research methodology of this survey paper. Section 3 presents a short analysis of background knowledge and main concepts used in the paper. Section 4 presents a representative set of developed museum chatbots and their evaluation focusing on the use of KGs and on human-like conversational features. Section 5 presents an evaluation of selected chatbot platforms, and their evaluation conducted with the use of the AHP evaluation method. Section 6 presents a novel architecture of a distributed, collaborative, graph-based conversational AI multi-chatbot system approach. Finally, Section 7 presents the first steps towards an implementation of the proposed architecture for Nikos Kazantzakis Museum in Crete. Section 8 concludes the paper.