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The AAAI‐18 workshop program offered 15 workshops covering a wide range of topics in AI. The workshops were held February 2–3, 2018, at the Hilton New Orleans Riverside in New Orleans, Louisiana. This report contains summaries of the Affective Content Analysis workshop; the Artificial Intelligence Applied to Assistive Technologies and Smart Environments; the AI and Marketing Science workshop; the Artificial Intelligence for Cyber Security workshop; the AI for Imperfect‐Information Games; the Declarative Learning Based Programming workshop; the Engineering Dependable and Secure Machine Learning Systems workshop; the Health Intelligence workshop; the Knowledge Extraction from Games workshop; the Plan, Activity, and Intent Recognition workshop; the Planning and Inference workshop; the Preference Handling workshop; the Reasoning and Learning for Human‐Machine Dialogues workshop; and the the AI Enhanced Internet of Things Data Processing for Intelligent Applications workshop.

The Workshop Program of the Association for the Advancement of Artificial Intelligence's Thirtieth AAAI Conference on Artificial Intelligence (AAAI‐16) was held at the beginning of the conference, February 12–13, 2016. Workshop participants met and discussed issues with a selected focus, and the workshop provided an informal setting for active exchange among researchers, developers, and users on topics of current interest. The AAAI‐16 workshops were an excellent forum for exploring emerging approaches and task areas, for bridging the gaps between AI and other fields or between subfields of AI, for elucidating the results of exploratory research, or for critiquing existing approaches. The 15 workshops held at AAAI‐16 were Artificial Intelligence Applied to Assistive Technologies and Smart Environments (WS‐16‐01), AI, Ethics, and Society (WS‐16‐02), Artificial Intelligence for Cyber Security (WS‐16‐03), Artificial Intelligence for Smart Grids and Smart Buildings (WS‐16‐04), Beyond NP (WS‐16‐05), Computer Poker and Imperfect Information Games (WS‐16‐06), Declarative Learning Based Programming (WS‐16‐07), Expanding the Boundaries of Health Informatics Using AI (WS‐16‐08), Incentives and Trust in Electronic Communities (WS‐16‐09), Knowledge Extraction from Text (WS‐16‐10), Multiagent Interaction Without Prior Coordination (WS‐16‐11), Planning for Hybrid Systems (WS‐16‐12), Scholarly Big Data: AI Perspectives, Challenges, and Ideas (WS‐16‐13), Symbiotic Cognitive Systems (WS‐16‐14), and World Wide Web and Population Health Intelligence (WS‐16‐15).

AbstractThere has long been a concern that rating building thermal performance based on predicted space loads in conditioned mode is inappropriate to achieve overall energy efficiency of houses in temperate climates. Buildings designed to be free running may achieve better results using a more appropriate rating system. This study, using simulation, predicts the thermal performance of houses in two different operation modes: conditioned and free running. The thermal performance of houses in the conditioned mode is indicated by predicted annual energy requirements (MJ/m2). Thermal performance in the free-running mode is indicated by annual degree discomfort hours (DDH). The paper investigates the correlation between the indicator of thermal performance of simulated houses in a conditioned operation mode and the indicator of thermal performance of those houses in a free-running operation mode. Despite a strong relationship between these two indicators, some significant differences become clear leading to a discussion of the persistent technical problems and issues that are encountered when attempting to optimize energy efficient architectural designs. The results of this study confirm the necessity of a new House Rating Scheme (HRS) incorporating an appropriate indicator for free-running buildings.