This paper addresses a data integration problem: given several mutually consistent datasets each of which measures a subset of the variables of interest, how can one construct a probabilistic model that fits the data and gives reasonable answers to questions which are under-determined by the data? Here we show how to obtain a Bayesian network model which represents the unique probability function that agrees with the probability distributions measured by the datasets and otherwise has maximum entropy. We provide a general algorithm, OBN-cDS, which offers substantial efficiency savings over the standard brute-force approach to determining the maximum entropy probability function. Furthermore, we develop modifications to the general algorithm which enable further efficiency savings but which are only applicable in particular situations. We show that there are circumstances in which one can obtain the model (i) directly from the data; (ii) by solving algebraic problems; and (iii) by solving relatively simple independent optimisation problems.

'17) was hosted at the Georgia Institute This was the third time the conference had been hosted in North America (Mexico City, ICCC'11; Park City, ICCC'15), and the Georgia Institute of Technology and local hosts provided extremely comfortable accommodation for everyone, furthering the traditional friendly and welcoming atmosphere of the conference. Thirty-four full papers were presented in a single track over three and a half days, as oral presentations, or posters and short talks, depending on the nature of the contribution. The papers were grouped by theme. A foundations session opened the conference with talks on application domains in CC, building a CC system, and teaching CC. A language session followed, looking at linguistic creativity in narrative and poetry.

We review the 2014 International Planning Competition (IPC-2014), the eighth in a series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess state-of-the-art in three prominent areas of planning research: the deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic part (IPPC). Each part evaluated planning systems in ways that pushed the edge of existing planner performance by introducing new challenges, novel tasks, or both. The competition surpassed again the number of competitors than its predecessor, highlighting the competition’s central role in shaping the landscape of ongoing developments in evaluating planning systems.