Research has demonstrated the efficacy of closed-loop control of anesthesia using the bispectral index (BIS) of the electroencephalogram as the controlled variable, and the development of model-based, patient-adaptive systems has considerably improved anesthetic control. To further explore the use of model-based control in anesthesia, we investigated the application of reinforcement learning (RL) in the delivery of patient-specific, propofol-induced hypnosis in human volunteers. When compared to published performance metrics, RL control demonstrated accuracy and stability, indicating that further, more rigorous clinical study is warranted.

Developing a robust, flexible, closed-loop walking algorithm for a humanoid robot is a challenging task due to the complex dynamics of the general biped walk. Common analytical approaches to biped walk use simplified models of the physical reality. Such approaches are partially successful as they lead to failures of the robot walk in terms of unavoidable falls. Instead of further refining the analytical models, in this work we investigate the use of human corrective demonstrations, as we realize that a human can visually detect when the robot may be falling. We contribute a two-phase biped walk learning approach, which we experiment on the Aldebaran NAO humanoid robot. In the first phase, the robot walks following an analytical simplified walk algorithm, which is used as a black box, and we identify and save a walk cycle as joint motion commands. We then show how the robot can repeatedly and successfully play back the recorded motion cycle, even if in open-loop. In the second phase, we create a closed-loop walk by modifying the recorded walk cycle to respond to sensory data. The algorithm learns joint movement corrections to the open-loop walk based on the corrective feedback provided by a human, and on the sensory data, while walking autonomously. In our experimental results, we show that the learned closed-loop walking policy outperforms a hand-tuned closed-loop policy and the open-loop playback walk, in terms of the distance traveled by the robot without falling.

The next decade will see an abundance of new intelligent systems, many of which will be market-based. Soon, users will interact with many new markets, perhaps without even knowing it: when driving their car, when listening to a song, when backing up their files, or when surfing the web. We argue that these new systems can only be successful if a new approach is chosen towards designing them. In this paper we introduce the general problem of "Hidden Market Design." The design of a "weakly hidden" market involves reducing some of the market complexities and providing a user interface (UI) that makes the interaction seamless for the user. A "strongly hidden market" is one where some semantic aspect of a market is hidden altogether (e.g., budgets, prices, combinatorial constraints). We show that the intersection of UI design and market design is of particular importance for this research agenda. To illustrate hidden market design, we give a series of potential applications. We hope that the problem of hidden market design will inspire other researchers and lead to new research in this direction, paving the way for more successful market-based systems in the future.

Motivated by a real-world problem, we study a novel budgeted optimization problem where the goal is to optimize an unknown function f ( x ) given a budget. In our setting, it is not practical to request samples of  f ( x ) at precise input values due to the formidable cost of precise experimental setup. Rather, we may request a constrained experiment, which is a subset r of the input space for which the experimenter returns  x  in r and  f ( x ). Importantly, as the constraints become looser, the experimental cost decreases, but the uncertainty about the location  x  of the next observation increases. Our goal is to manage this trade-off by selecting a sequence of constrained experiments to best optimize f within the budget. We introduce cost-sensitive policies for selecting constrained experiments using both model-free and model-based approaches, inspired by policies for unconstrained settings. Experiments on synthetic functions and functions derived from real-world experimental data indicate that our policies outperform random selection, that the model-based policies are superior to model-free ones, and give insights into which policies are preferable overall.

Within the electric power literature the transmission expansion planning problem (TNEP) refers to the problem of how to upgrade an electric power network to meet future demands. As this problem is a complex, non-linear, and non-convex optimization problem, researchers have traditionally focused on approximate models of power flows. Existing approaches are often tightly coupled to the approximation choice. Until recently, these approximations have produced results that are straight-forward to adapt to the more complex (real) problem. However, the power grid is evolving towards a state where the adaptations are no longer easy (e.g. large amounts of limited control, renewable generation) that necessitates new optimization techniques. In this paper, we propose a local search variation of the powerful Limited Discrepancy Search (LDLS) that encapsulates the complexity of power flows in a black box that may be queried for information about the quality of a proposed expansion. This allows the development of a new optimization algorithm that is independent of the underlying power model.

Unifying logical and probabilistic reasoning is a longstanding goal of AI. While recent work in lifted belief propagation, handling whole sets of indistinguishable objects together, are promising steps towards achieving this goal that even scale to realistic domains, they are not tailored towards solving combinatorial problems such as determining the satisfiability of Boolean formulas. Recent results, however, show that certain other message passing algorithms, namely, survey propagation, are remarkably successful at solving such problems. In this paper, we propose the first lifted variants of survey propagation and its simpler version warning propagation. Our initial experimental results indicate that they are faster than using lifted belief propagation to determine the satisfiability of Boolean formulas.

Lifting can greatly reduce the cost of inference on first-order probabilistic graphical models, but constructing the lifted network can itself be quite costly. In online applications (e.g., video segmentation) repeatedly constructing the lifted network for each new inference can be extremely wasteful, because the evidence typically changes little from one inference to the next. The same is true in many other problems that require repeated inference, like utility maximization, MAP inference, interactive inference, parameter and structure learning, etc. In this paper, we propose an efficient algorithm for updating the structure of an existing lifted network with incremental changes to the evidence. This allows us to construct the lifted network once for the initial inference problem, and amortize the cost over the subsequent problems. Experiments on video segmentation and viral marketing problems show that the algorithm greatly reduces the cost of inference without affecting the quality of the solutions.

Probabilistic programing is an emerging field at the intersection of statistical learning and programming languages. An appealing property of probabilistic programming languages (PPL) is their support for constructing arbitrary probability distributions. This allows one to model many different domains and solve a variety of problems. We show the link between probabilistic planning and PPLs by introducing a translation that allows one to map probabilistic planning problems onto parameter learning in PPLs. The advantage of our approach is twofold. Firstly, having the expressivity of a programming language simplifies modeling compared to using existing planning languages such as PPDDL. Secondly, there exist effective general-purpose learning algorithms that — having the correct encoding — can readily be used to learn optimal policies. In this paper we use ProbLog — a probabilistic version of Prolog — as programming language, but our approach can be applied on any other PPL as well.

Providing energy conservation services becomes a hot research topic because more and more people attach importance to environmental protection. This research proposes a framework that consists of four process models: appliance recognition, activity-appliances model, unattended appliances detection, and energy conservation service. Appliance recognition model can recognizes the operating states of appliances from raw sensing data of electric power. An activity-appliances model has been built to associate activities with appliances according to the data of Open Mind Common Sense Project. Using the relationship between activities can help to detect unattended appliances, which are consuming electric power but not take part in the resident’s activities. After obtain information of appliance operating states and unattended appliances, residents can receive energy conservation services for notifying the energy consumption information. Finally, the experimental results show that dynamic Baysian network approach can achieve higher than 92% accuracy for appliance recognition. Data of activity-appliances model shows most appliances are strong activity-related.

Visual feedback is an important approach in persuasive technology. We present four significant design dimensions of persuasive feedback systems. We investigate several previous notable projects and find out the underlying metaphorical structures within them. We analyze the meaning of metaphor in the persuasive feedback design, and examine how metaphor is being used. The results tell us that metaphor analysis plays a useful role in interpreting the creativity of visual design in the persuasive feedback system.