Malfunctioning HVAC equipment in commercial buildings wastes between 15% and 30% of energy. Many diagnosis approaches tackle this problem, but they either suffer from a lack of detailed fault information or a lack of adaptability to different buildings and equipment. Clearly, especially in the light of an ever increasing amount of sensor data that is available in heavily metered smart buildings, easily adaptable self learning in-depth diagnosis approaches are needed. This paper addresses the challenges of developing such approaches and describes the contribution artificial intelligence techniques like transfer learning, ontologies, knowledge representation or diagnosis can make in overcoming these challenges.

This paper presents a novel scoring rule-based mechanism that encourages agents to produce costly estimates of future events and truthfully report them to a centre when the budget for payments to the agents is itself determined by their reports. This is applied to a model of aggregated demand prediction within a microgrid where, given estimates of future consumptions, an aggregator must optimally purchase electricity for a set of homes, each represented by self-interested, rational home agents. This in turn reduces the need for costly standby generation within the grid. The aggregator has prior information about the amount each home will consume, and determines the amount to pay each agent based on savings resulting from using the agents' reported information, over its own prior information. Agents use sensory information regarding their property and its occupants to generate these estimates, which they transmit to the aggregator using smart grid technology. The proposed mechanism is dominant strategy incentive compatible and empirical evaluation shows that it encourages agents to exert effort in producing precise estimates. We show that the mechanism is ex ante individually rational for the aggregator, and that it outperforms a simpler mechanism whereby savings are distributed evenly.

End-user programming raises the possibility that the people who know best what a software system should do will be able to customize, remedy original programming defects and adapt systems as requirements change. As computing increasingly enters the home and workplace, the need for such tools is high, but state of practice approaches offer very limited capability. We describe the Interactive Bootstrapped Learning (iBL) system which allows users to modify code by interactive teaching similar to human instruction. It builds on an earlier system focused on exploring how machine learning can be used to compensate for limited instructional content. iBL provides an end-to-end solution in which user-iBL dialog gradually refines a hypothesis about what transformation to a target code base will best achieve user intent. The approach integrates elements of many AI technologies including machine learning, dialog management, AI planning and automated model construction.

Humans are confronted with an increasingly complex array of ingestion substances and dietary choices that influence health and well being. However, even with strong medical evidence that clearly links ingestion strategies and heath consequences, the general public struggles to make health-optimizing ingestion decisions. Based on our literature review, we delineate a typology of barriers to formulating health-optimizing ingestion strategies. We propose that the introduction of artificial intelligence (AI) as “decision management” (AI-DM) technology into the ingestion decision-making network would increase the likelihood of more predictable and optimized health outcomes. Also, we delineate the key informational constituencies needed to enable a comprehensive and effective AI-DM system. While no author has yet proposed AI in the particular context discussed in this paper, the theoretical and empirical literature suggests that this might be possible. We conclude by discussing areas for additional research.

We present a model of interactive activity recognition and prompting for use in an assistive system for persons with cognitive disabilities. The system can determine the user’s state by interpreting sensor data and/or by explicitly querying the user, and can prompt the user to begin or end tasks. The objective of the system is to help the user maintain a daily schedule of activities while minimizing interruptions from questions or prompts. The model is built upon an option-based hierarchical POMDP. Options can be programmed and customized to specify complex routines for prompting or questioning. Novel aspects of the model include (1) the introduction of adaptive options, which employ a lightweight user model and are able to provide near-optimal performance with little exploration; (2) a restricted-inquiry dual-control algorithm that can appeal for help from the user when sensor data is ambiguous; and (3) a combined filtering / most likely-sequence algorithm for activities determining the beginning and ending time points of the user’s activities. Experiments show that each of these features contributes to the robustness of the model.

This paper considers a setting where a single ``leadership agent'' intervenes in a multi-agent system through actions that (perhaps subtly) change the dynamics of the system. We describe a number of forms this intervention can take and compare these situations to settings in previous work. We identify two important effects of leadership: faster system convergence, and convergence to a better equilibrium. Empirically, we first explore these properties in leadership of algorithms engaged in classical 2-player games. We then apply this general framework to the leadership of a super-peer file-sharing network. In these experiments the network contains some agents that make locally greedy decisions that hamper the network as a whole. We show that a leader acting based on a more global criteria can push the system to a better equilibrium point as well as speeding up convergence. We also show how a mathematical approximation of such super-peer networks can be used to aid a leader in determining a minimum-cost intervention strategy.

Traditionally, security decisions have been made without explicitly accounting for adaptive, intelligent attackers. Recent game theoretic security models have explicitly included attacker response in computing randomized security policies. Techniques to date, however, generally fail to explicitly account for interdependence between the targets to be secured, which is of vital importance in a variety of domains, including cyber, supply chain, and critical infrastructure security. We introduce a novel framework for computing optimal randomized security policies in networked domains which extends previous approaches in two ways. First, we extend previous linear programming techniques for Stackelberg security games to incorporate benefits and costs of arbitrary security configurations on individual assets. Second, we offer a principled model of failure cascades that allows us to capture both the direct and indirect value of assets. Finally, we use our framework to analyze four models, two based on random graph generation models, a simple model of interdependence between critical infrastructure and key resource sectors, and a model of the Fedwire interbank payment network.

While microblogging has gained popularity on the Internet, analyzing and processing short messages has become a challenging task in natural language processing. This paper analyzes the differences between Internet short messages (or “microtext”) and general articles by comparing the Plurk Corpus and the Sinica Balanced Corpus. Likelihood ratio and the tóngyìcícílín thesaurus are adopted to analyze the lexical semantics of frequent terms in each corpus. Furthermore, the NTUSD sentiment dictionary is used to compare the sentiment distribution of the two corpora. The result is also applied to sentiment transition analysis.

We present in this paper, the application of a novel approach to computational modeling, understanding and detection of social phenomena in online multi-party discourse. A two-tiered approach was developed to detect a collection of social phenomena deployed by participants, such as topic control, task control, disagreement and involvement. We discuss how the mid-level social phenomena can be reliably detected in discourse and these measures can be used to differentiate participants of online discourse. Our approach works across different types of online chat and we show results on two specific data sets.

Analyzing chat traffic has important applications for both the military and the civilian world. This paper presents a case study of a real-world application of chat analysis in support of team training exercise in the military. It compares the results of an unsupervised learning approach with those of a supervised classification approach. The paper also discusses some of the specific challenges presented by this domain.