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Conversational corpora based on human-human dialogues have often been used for training of data-driven dialogue systems. However, human-human conversations might not be the optimal inputs for machine learning training aims used in HRI. This paper suggests the creation of a conversational corpus based on Human-Robot conversations as input for the training of a dialogue system used in future conversational robots. We propose that the significant differences between Human-Human Conversation (HHC) and Human-Robot Conversation (HRC) in terms of used language and other aspects (e.g., humanlikeness, embodiment, etc.) might affect the quality of the responses from a conversational robot. Hence, the use of HRCs as an input could improve the responses of the robots when the conversational machine learning system is trained using a more realistic model of HRI conversations rather than a HHI model. Future applications of conversational robots in education and health care could be enhanced by using an appropriate HRC corpus.

Jesús Favela,1 Luis A Castro,2 Francisco Franco-Marina,3 Sergio Sánchez-García,4 Teresa Juárez-Cedillo,4 Claudia Espinel Bermudez,4 Julia Mora-Altamirano,4 Marcela D Rodriguez,5 Carmen García-Peña41Center for Scientific Research and Higher Education of Ensenada, Ensenada, Baja California, Mexico; 2Sonora Institute of Technology, Ciudad Obregon, Mexico; 3National Institute of Respiratory Diseases, Mexican Ministry of Health, Mexico City, Mexico; 4Epidemiologic and Health Service Research Unit, Aging Area, XXI Century National Medical Center, Mexican Institute of Social Security, Mexico City, Mexico; 5School of Engineering, MyDCI, Autonomous University of Baja California, Mexicali, MexicoObjective: To assess whether an intervention based on nurse home visits including alert buttons (NV+AB) is effective in reducing frailty compared to nurse home visits alone (NV-only) and usual care (control group) for older adults.Design: Unblinded, randomized, controlled trial.Setting: Insured population covered by the Mexican Social Security Institute living in the city of Ensenada, Baja California, Mexico.Participants: Patients were aged over 60 years with a frailty index score higher than 0.14.Intervention: After screening and informed consent, participants were allocated randomly to the control, NV+AB, or NV-only groups.Measurements: The primary outcome was the frailty score 9 months later. Quality of life, depression, comorbidities, health status, and health service utilization were also considered.Results: The framing sample included 819 patients. Of those, 591 were not located because they did not have a landline/telephone (341 patients), they had died (107), they were ill (50), or they were not currently living in the city (28). A screening interview was applied to 228 participants, and 57 had a score ≤0.14, 171 had ≥0.14, and 16 refused to complete the baseline questionnaire. A home visit was scheduled for 155 patients. However, 22 did not complete the baseline questionnaire. The final 133 subjects were randomized into the NV+AB (n = 45), NV-only (n = 44), and control (n = 44) groups. There were no statistically significant differences in the baseline characteristics of the groups. The mean age overall was 76.3 years (standard deviation 4.7) and 45% were men. At the baseline, 61.65% were classified as frail. At end of follow-up the adjusted prevalence of frailty in NV+AB group was 23.3% versus 58.3% in the control group.Conclusion: An intervention based on NV+AB seems to have a positive effect on frailty scores.Keywords: gerontechnology, frailty, elderly

Hybrid recommender systems are capable of providing better rec-ommendations than non-hybrid ones. Our approach to hybrid recommenders is the use of prediction strategies that determine which prediction technique(s) should be used at the moment an actual prediction is required. In this paper, we determine whether case-based reasoning can provide more accurate prediction strategies than rule-based predictions strategies created manually by experts. Experiments show that case-based reasoning can indeed be used to create pre-diction strategies; it can even increase the accuracy of the recommender in sys-tems where the accuracy of the used prediction techniques is highly spread.

Information gathering in Internet is a complex activity. A solution consists in to assist Internet users in their information gathering processes by means of distributed intelligent agents in order to find the fittest information to their information needs. In this paper we describe a fuzzy linguistic multi-agent model that incorporates information filtering techniques in its structure, i.e., a collaborative filtering agent. In such a way, the information filtering possibilities of multi-agent system on the Web are increased and its retrieval results are improved.

This paper describes the findings of an experimental research concentrating on collaboration in a multi-player video game. The overall goal is to study the cognitive impacts of the awareness tools. The focus is in finding an effect on performance as well as on the representation an individual build of what his partner knows, plans and intends to do (i.e. Mutual Modelling). Using an awareness tools has a significant effect by improving task performance. However, the players who were provided with this tool did not show any improvement of their mutual modelling. Further analysis on contrasted groups revealed that there was an effect of the awareness tool on mutual modelling for players who spent a large amount of time using the tool.