We describe a proposed assistive conversational skills training system using artificial intelligence and natural language generation techniques to simulate spoken dialogue between two embodied agents, one character representing a caregiver and the other character a person with Alzheimer's Disease (AD). The type of dialogue simulated is social conversation in which the caregiver encourages the person with AD to tell autobiographical stories. Intervention by the caregiver may be required at times to keep the conversation going due to linguistic or cognitive problems experienced by the storyteller. In our proposal, turn and topic management behavior for the storyteller character is implemented by combining rules representing normal pragmatic routines with linguisticallymotivated rules representing coping strategies.
Yoshikawa, Yuicho (Osaka University) | Iio, Takamasa (Osaka University) | Arimoto, Tsunehiro (Osaka University) | Sugiyama, Hiroaki (NTT Communication Science Laboratories) | Ishiguro, Hiroshi (Osaka University)
In this position paper, we address potential merits of a novel conversational system using the group form of mul-tiple robots that provides users with a stronger sense of conversation, with which a person can feel as if he or she is participating in a conversation. The merits can be per-formed by implementing the group behavior of multiple robots so that appropriate turn-taking is inserted to en-hance the sense of conversation against potential conver-sational break-down. Through introducing the preliminary analysis of three experiments, how the sense of conversa-tion can be enhanced and evaluated is exemplified and its limitations and potentials are argued.
Dorr, Bonnie J. (Institute for Human and Machine Cognition) | Galescu, Lucian (Institute for Human and Machine Cognition) | Perera, Ian (Institute for Human and Machine Cognition) | Hollingshead-Seitz, Kristy (Institute for Human and Machine Cognition) | Atkinson, David (Institute for Human and Machine Cognition) | Clark, Micah (Institute for Human and Machine Cognition) | Clancey, William (Institute for Human and Machine Cognition) | Wilks, Yorick ( Institute for Human and Machine Cognition ) | Fosler-Lussier, Eric (Ohio State University)
This Blue Sky presentation focuses on a major shift toward a notion of “ambient intelligence” that transcends general applications targeted at the general population. The focus is on highly personalized agents that accommodate individual differences and changes over time. This notion of Extended Ambient Intelligence (EAI) concerns adaptation to a person’s preferences and experiences, as well as changing capabilities, most notably in an environment where conversational engagement is central. An important step in moving this research forward is the accommodation of different degrees of cognitive capability (including speech processing) that may vary over time for a given user—whether through improvement or through deterioration. We suggest that the application of divergence detection to speech patterns may enable adaptation to a speaker’s increasing or decreasing level of speech impairment over time. Taking an adaptive approach toward technology development in this arena may be a first step toward empowering those with special needs so that they may live with a high quality of life. It also represents an important step toward a notion of ambient intelligence that is personalized beyond what can be achieved by mass-produced, one-size-fits-all software currently in use on mobile devices.
Securing access to sufficient quantities of these often scarce compounds is frequently a key impediment to demonstrating their clinical potential. On page 218 of this issue, Wender et al. (4) report a new approach to addressing supply issues associated with the marine natural product bryostatin 1--an efficient chemical synthesis. This synthesis provides increased access to this potent protein kinase C modulator and should facilitate continued clinical investigation of bryostatin 1 as an anticancer agent, Alzheimer's treatment, and HIV latency-reversing agent (5).