Such dialogs contain both cooperative and adversarial elements, requiring agents to understand and formulate long-term plans and generate utterances to achieve their goals. The FAIR researchers' key technical innovation in building such long-term planning dialog agents is an idea called dialog rollouts. The second model is fixed, because the researchers found that updating the parameters of both agents led to divergence from human language as the agents developed their own language for negotiating. This work represents an important step for the research community and bot developers toward creating chatbots that can reason, converse, and negotiate, all key steps in building a personalized digital assistant.

If human-robot interaction is mainly shaped by users' strategies to deal with their unfamiliar artificial com munication partner, as it is suggested here, robot dialog design should orient at reducing users' uncertainty about the affordances of the robot and the joint task. Two experiments are presented that investigate the impact of verbal robot utterances on users' behavior; results show that users react sensitively to subtle linguistic cues that may guide them into appropriate understandings of the robot. Furthermore, the role of user expectations and robot appearance are discussed in the light of the model presented.

We present an innovative approach for large-scale data collection in human-robot interaction research through the use of online multi-player games. By casting a robotic task as a collaborative game, we gather thousands of examples of human-human interactions online, and then leverage this corpus of action and dialog data to create contextually relevant, social and task-oriented behaviors for human-robot interaction in the real world. We demonstrate our work in a collaborative search and retrieval task requiring dialog, action synchronization and action sequencing between the human and robot partners. A user study performed at the Boston Museum of Science shows that the autonomous robot exhibits many of the same patterns of behavior that were observed in the online dataset and survey results rate the robot similarly to human partners in several critical measures.

This special issue of AI Magazine on dialog with robots brings together a collection of articles on situated dialog. The contributing authors have been working in interrelated fields of human-robot interaction, dialog systems, virtual agents, and other related areas and address core concepts in spoken dialog with embodied robots or agents. Several of the contributors participated in the AAAI Fall Symposium on Dialog with Robots, held in November 2010, and several articles in this issue are extensions of work presented there. The articles in this collection address diverse aspects of dialog with robots, but are unified in addressing opportunities with spoken language interaction, physical embodiment, and enriched representations of context.

Modeling interaction with robots raises new and different challenges for dialog modeling than traditional dialog modeling with less embodied machines. We present four case studies of implementing a typical human-robot interaction scenario with different state-of-the-art dialog frameworks in order to identify challenges and pitfalls specific to HRI and potential solutions. The results are discussed with a special focus on the interplay between dialog and task modeling on robots.