We design iterative receiver schemes for a generic wireless communication system by treating channel estimation and information decoding as an inference problem in graphical models. We introduce a recently proposed inference framework that combines belief propagation (BP) and the mean field (MF) approximation and includes these algorithms as special cases. We also show that the expectation propagation and expectation maximization algorithms can be embedded in the BP-MF framework with slight modifications. By applying the considered inference algorithms to our probabilistic model, we derive four different message-passing receiver schemes. Our numerical evaluation demonstrates that the receiver based on the BP-MF framework and its variant based on BP-EM yield the best compromise between performance, computational complexity and numerical stability among all candidate algorithms.

Smart cities and smart grids heavily depend on data being exchanged between a large number of heterogeneous entities. Parts of the data which such systems depend on are relevant to the privacy of individuals, e.g., data about energy consumption or current location. We assume the use of semantic technologies for data representation and exchange, and express privacy requirements as formal policies. We take a data-centric view, that is, we attach policies that restrict isolated uses of data to the data directly. When systems exchange data, they also exchange the policies pertaining to the exchanged data. The main benefit of such an approach over a system-level view is that our data-centric approach works in scenarios without central control.

The (Shapley-Scarf) housing market is a well-studied and fundamental model of an exchange economy. Each agent owns a single house and the goal is to reallocate the houses to the agents in a mutually beneficial and stable manner. Recently, Alcalde-Unzu and Molis (2011) and Jaramillo and Manjunath (2011) independently examined housing markets in which agents can express indifferences among houses. They proposed two important families of mechanisms, known as TTAS and TCR respectively. We formulate a family of mechanisms which not only includes TTAS and TCR but also satisfies many desirable properties of both families. As a corollary, we show that TCR is strict core selecting (if the strict core is non-empty). Finally, we settle an open question regarding the computational complexity of the TTAS mechanism. Our study also raises a number of interesting research questions.

A robot coexisting with humans must not only be able to successfully perform physical tasks, but must also be able to interact with humans in a socially appropriate manner. In many social settings, this involves the use of social signals like gaze, facial expression, and language. In this paper we discuss preliminary work focusing on the problem of combining social interaction with task-based action in a dynamic, multiagent bartending domain, using an embodied robot. We discuss how social states are inferred from low-level sensors, using vision and speech as input modalities, and present a planning approach that models task, dialogue, and social actions in a simple bartending scenario. This approach allows us to build interesting plans, which have been evaluated in a real-world study with human subjects, using a general purpose, off-the-shelf planner, as an alternative to more mainstream methods of interaction management.

One of the key issues for transportation companies is to produce an optimal plan for the work of crew members. Crew planning consists of a sequence of phases, the first two corresponding to planning duties (sequences of trips to be done by crew members from their home base to their home base) and planning rosters (sequences of duties and rest days to be followed by crew members during a certain number of weeks). Both duty and roster planning are subject to a large number of constraints. Duty planning is constrained by intra-duty constraints and roster planning by inter-duty constraints. Since inter-duty constraints relate how duties can be combined into a roster, it is desirable that some of these constraints be transposed into the duty planning phase, as additional constraints, to guarantee that the duties produced in the first phase are "rosterable'' in the second phase. Both Artificial Intelligence (AI) and Operations Research (OR) have addressed duty planning, but for very large scale problems, OR has been far more successful due to its global vision of the problem. This paper discusses the use of AI local search to improve an OR-based duty planning optimizer that uses additional constraints.

In 2007 we presented a paper that described the application of Natural Language Processing (NLP) and Machine Translation (MT) for the automated translation of process build instructions from English to other languages to support Ford’s assembly plants in non-English speaking countries. This project has continued to evolve with the addition of new languages and improvements to the translation process. However, we discovered that there was a large demand for automated language translation across all of Ford Motor Company and we decided to expand the scope of our project to address these requirements. This paper will describe our efforts to meet all of Ford’s internal translation requirements with AI and MT technology and focus on the challenges and lessons that we learned from applying advanced technology across an entire corporation.

We present a methodology for the deployment of the max-sum algorithm, a well known decentralised algorithm for coordinating autonomous agents, for problems related to situational awareness. In these settings, unmanned autonomous vehicles are deployed to collect information about an unknown environment. Our methodology then helps identify the choices that need to be made to apply the algorithm to these problems. Next, we present a case study where the methodology is used to develop a system for disaster management in which a team of unmanned aerial vehicles coordinate to provide the first responders of the area of a disaster with live aerial imagery. To evaluate this system, we deploy it on two unmanned hexacopters in a variety of scenarios. Our tests show that the system performs well when confronted with the dynamism and the heterogeneity of the real world.

The Partner Units Problem (PUP) constitutes a challenging real-world configuration problem with diverse application domains such as railway safety, security monitoring, electrical engineering, or distributed systems.Although using the latest problem-solving methods including Constraint Programming, SAT Solving,Integer Programming, and Answer Set Programming, current methods fail to generate solutions for mid-sized real-world problems in acceptable time. This paper presents the QuickPup algorithm based on backtrack search combined with smart variable orderings and restarts. QuickPup outperforms the available methods by orders of magnitude and thus makes it possible toautomatically solve problems which couldn’t be solved without human expertise before. Furthermore, the runtimes of QuickPup are typically below one second for real-world problem instances.

In this paper we present DrillEdge — a commercial and award winning software system that monitors oil-well drilling operations in order to reduce non-productive time (NPT). DrillEdge utilizes case-based reasoning with temporal representations on streaming real-time data, pattern matching and agent systems to predict problems and give advice on how to mitigate the problems. The methods utilized, the architecture, the GUI and development cost in addition to two case studies are documented.

We have developed a method for statistical anomaly detection which has been deployed in a tool for condition monitoring of train fleets. The tool is currently used by several railway operators over the world to inspect and visualize the occurrence of event messages generated on the trains. The anomaly detection component helps the operators to quickly find significant deviations from normal behavior and to detect early indications for possible problems. The savings in maintenance costs comes mainly from avoiding costly breakdowns, and have been estimated to several million Euros per year for the tool. In the long run, it is expected that maintenance costs can be reduced with between 5 and 10 % by using the tool.