## 5 State of the Art in Conversational AI Platforms
### Platforms' Evaluation Using Analytic Hierarchical Process (AHP)
#### 5.4.1 Evaluation Attributes  
The selection of the evaluation attributes was driven (a) by our extensive study of related works [92, 117, 118, 123, 127] and (b) the design requirements of a KG-based, distributed, and collaborative multi-chatbot conversational AI system architecture. Such attributes comply with the needs of a medium-size museum, the main features of the examined platforms and the ability to be measurable and specifiable. Moreover, they comply with our research focus to design conversational agents that are based on KGs, they can collaborate to satisfy other agents (human or software) conversational needs and do this by utilizing available knowledge shared in a distributed manner. The specific attributes are:  
1. Knowledge Graphs (KGs). Use of KGs for representing and integration of specific domain (museum) and general knowledge.  
Natural Language Understanding (NLU). Use of NLU techniques to interpret natural language (that humans understand) to formal language (that machines understand) and the opposite.
3. API interaction (API). It is essential for developing a system that does not rely only on trained conversations that are built in the platform, but can connect, search and extract knowledge from external sources and databases.
4. Distributed collaboration (DC). All the components of the system must be designed to function autonomously but at the same time to be able to collaborate using a multi-chatbot network. This attribute also allows the smooth transfer of the designed components of a chatbot to any other platform.
5. License (LS). Examine if the use of a fully open-source platform is an adequate solution, or a paid version is more flexible for the developers.
6. Multilingual support (MS). The platforms must provide multi-lingual support for the encoded conversations, especially in environments where users come from multilingual communities, as tourists visiting a museum. Moreover, the platforms must allow the extension to new languages.
7. Multiple channels support (MCS). The chatbots that are developed must be easily deployed via as many channels as possible, such as FB Messenger, WeChat, webpages, Twitter, Slack, Alexa, and others.
8. Machine Learning (ML). Use of ML techniques to train chatbots to understand simple or complex questions and provide the right answer with a high precision.
9. Additional capabilities (AC). In this attribute we can add the sentiment analysis, the ability of adding personality traits to the chatbot and other special features.
10. Text-to-Speech (TTS) and Speech-to-Text (STT). The platform must be able to translate speech to text and the opposite. The ability must be either an in-house ability or to be provided (via APIs) by external applications.
11. Self-configuration (SC). This attribute is controversial as self-configuration of chatbots is essential for museums with limited IT support to easily manage the deployed chatbots. On the other hand, this feature does not give the developers much flexibility to use or test other components and integrate them to the chatbot solution.