Conversational ability is a mark of humanity and sentience. Conversational agents, known by a variety of different names, such as conversational bots, chatbots, intelligent virtual assistants, virtual customer assistants, dialogue systems, digital assistants, virtual agents, or virtual interfaces, are all growing in popularity. Conversational agents are a game-changer in supporting resource-limited human customer service, as well as expanding natural interfaces for ambient technologies, such as autonomous cars, smart homes, and consumer electronics.

However, the existing design and implementation of such systems, whether speech-based or text-based, are still primarily dependent upon conventional natural language processing technology: natural language understanding, the context in which the dialogue is used, dialogue management, and natural language generation. Applications are often tied to a specific domain to manage dialogue management strategies and maintain a dynamic knowledge database. Due to the mobility and dynamic environments of human users, it is important for conversational agents to offer the possibility to communicate through mobile entities such as mobile phones, smartphones, laptops, tablets, or palmtops. It is therefore crucial to consider context-awareness approaches based on mobile conversational agents, as well as how data can be collected from such contexts for analyzing behaviors to improve user experience. To promote robustness, corpus-based or machine learning techniques have been introduced to address the bottleneck in knowledge-based designs. The development of conversational agents has also been accelerated by the availability of no-code technologies in building conversational agents.

This Special Issue invites submissions that feature original research, both theoretical and practical, in designing, developing, and evaluating open-domain or domain-specific conversational agents. We also welcome review articles.

Potential topics include but are not limited to the following:

• Comparing knowledge-based and corpus-based approaches for building conversational agents
• Dialogue management strategies in conversational agents
• Dialogue modeling, including dialogue acts, turn-taking, and grounding
• Ontologies and knowledge-based design and implementation for conversational agents
• Conversational dynamics
• Evaluation strategies
• Tools and platforms for building conversational agents
• User experience and conversational agents
• Computational intelligence approaches to dialogue formulation and management Conversational agents and neuroscience
• Context awareness in mobile conversational agents
• Applications of mobile conversational agents
• Ubiquitous environments for wearable and embodied conversational agents
• Social, ethical, and legal implications of conversational agents in society