In previous work, we proposed a modal fragment of the situation calculus called ES, which fully captures Reiter's basic action theories. ES also has epistemic features, including only-knowing, which refers to all that an agent knows in the sense of having a knowledge base. While our model of only-knowing has appealing properties in the static case, it appears to be problematic when actions come into play. First of all, its utility seems to be restricted to an agent's initial knowledge base. Second, while it has been shown that only-knowing correctly captures default inferences, this was only in the static case, and undesirable properties appear to arise in the presence of actions.  In this paper, we remedy both of these shortcomings and propose a new dynamic semantics of only-knowing, which is closely related to Lin and Reiter's notion of progression when actions are performed and where defaults behave properly.

Despite the success of Gaussian processes (GPs) in modelling spatial stochastic processes, dealing with large datasets is still challenging. The problem arises by the need to invert a potentially large covariance matrix during inference. In this paper we address the complexity problem by constructing a new stationary covariance function (Mercer kernel) that naturally provides a sparse covariance matrix. The sparseness of the matrix is defined by hyper-parameters optimised during learning. The new covariance function enables exact GP inference and performs comparatively to the squared-exponential one, at a lower computational cost. This allows the application of GPs to large-scale problems such as ore grade prediction in mining or 3D surface modelling. Experiments show that using the proposed covariance function, very sparse covariance matrices are normally obtained which can be effectively used for faster inference and less memory usage.

Emerging service-oriented technologies allow software agents to automatically procure distributed services to complete complex tasks. However, in many application scenarios, service providers demand financial remuneration, execution times are uncertain and consumers have  deadlines for their tasks. In this paper, we address these issues by developing a novel approach that dynamically procures multiple, redundant services over time, in order to ensure success by the deadline. Specifically, we first present an algorithm for finding optimal procurement solutions, as well as a heuristic algorithm that achieves over 99% of the optimal and is capable of handling thousands of providers. Using experiments, we show that these algorithms achieve an improvement of up to 130% over current strategies that procure only single services. Finally, we consider settings where service costs are not known to the consumer, and introduce several mechanisms that incentivise providers to reveal their costs truthfully and that still achieve up to 95% efficiency.

Branch and bound is an effective technique for solving constraint optimization problems (COP’s). However, its search space expands very rapidly as the domain sizes of the problem variables grow. In this paper, we present an algorithm that clusters the values of a variable’s domain into sets. Branch and bound can then branch on these sets of values rather than on individual values, thereby reducing the branching factor of its search space. The aim of our clustering algorithm is to construct a collection of sets such that branching on these sets will still allow effective bounding. In conjunction with the reduced branching factor, the size of the explored search space is thus significantly reduced. We test our method and show empirically that it can yield significant performance gains over existing stateof- the-art techniques.

Canonical correlation analysis (CCA) and partial least squares (PLS) are well-known techniques for feature extraction from two sets of multi-dimensional variables. The fundamental difference between CCA and PLS is that CCA maximizes the correlation while PLS maximizes the covariance. Although both CCA and PLS have been applied successfully in various applications, the intrinsic relationship between them remains unclear. In this paper, we attempt to address this issue by showing the equivalence relationship between CCA and orthonormalized partial least squares (OPLS), a variant of PLS. We further extend the equivalence relationship to the case when regularization is employed for both sets of variables. In addition, we show that the CCA projection for one set of variables is independent of the regularization on the other set of variables. We have performed experimental studies using both synthetic and real data sets and our results confirm the established equivalence relationship. The presented analysis provides novel insights into the connection between these two existing algorithms as well as the effect of the regularization.

We present a method to decompose a declarative knowledge base, given by a logic program under Answer Set Semantics with access to external sources. It overcomes the ineffectiveness of current methods due to a lack of structural information about these sources, viewed as black boxes, by exploiting independency information in accesses to them.  To this end, we develop a generic notion of domain independence that allows to restrict the evaluation domain and, as a consequence, to prune unnecessary dependency assumptions between atoms. This leads to increased decomposability, which we demonstrate by an evaluation method for HEX-programs based on program rewriting. Experiments show that this may yield large performance gains. While developed for a particular formalism, the notions and ideas of this  paper might be adapted to related formalisms as well.

Practical data analysis relies on the ability to count observations of objectssuccinctly and efficiently. Unfortunately the space usage of an exact estimator grows with the size of the a priori set from which objects are drawn while the time required to maintain such an estimator grows with the size of the data set. We present static and on-line approximation schemes that avoid these limitations when approximate frequency estimates are acceptable. Our Log-Frequency Sketch extends the approximate counting algorithm of Morris [Morris1978] to estimate frequencies with bounded relative error via a single pass over a data set. It uses constant space per object when the frequencies follow a power law and can be maintained in constant time per observation. We give an (epsilon, delta)-approximation scheme which we verify empirically on a large natural language data set where, for instance, 95 percent of frequencies are estimated with relative error less than 0.25 using fewer than 11 bits per object in the static case and 15 bits per object on-line.

Conjunctive queries (CQ) are fundamental for accessing description logic (DL) knowledge bases. We study CQ answering in (extensions of) the DL EL, which is popular for large-scale ontologies and underlies the designated OWL2-EL profile of OWL2. Our main contribution is a novel approach to CQ answering that enables the use of standard relational database systems as the basis for query execution. We evaluate our approach using the IBM DB2 system, with encouraging results.

Resource allocation in computing clusters is traditionally centralized, which limits the cluster scale. Effective resource allocation in a network of computing clusters may enable building larger computing infrastructures. We consider this problem as a novel application for multiagent learning (MAL). We propose a MAL algorithm and apply it for optimizing online resource allocation in cluster networks. The learning is distributed to each cluster, using local information only and without access to the global system reward. Experimental results are encouraging: our multiagent learning approach performs reasonably well, compared to an optimal solution, and better than a centralized myopic allocation approach in some cases.

Our approach analyzes the data dependencies in the trace to discover the context of the actions that appear in the trace. Existing work on workflow mining ignores the Using the context information we can decide whether the two dataflow aspect of the problem. This is not acceptable occurrences of the same action correspond to the same node for service-oriented applications that use Web in the workflow or not. As a result, unlike the previous work services with typed inputs and outputs. We propose [van der Aalst et al., 2004; Cook and Wolf, 1998b; Agrawal a novel algorithm WIT (Workflow Inference from et al., 1998], we are able to learn workflows with non-unique Traces) which identifies the context similarities of action nodes. Furthermore, the context discovery can easily the observed actions based on the dataflow and uses be generalized to work with causal dependencies instead of model merging techniques to generalize the control data dependencies. Thus, the ideas presented in this work flow and the dataflow simultaneously. We identify can be applied to other areas such as learning domain specific the class of workflows that WIT can learn correctly.