Semi-supervised learning algorithms commonly incorporate the available background knowledge such that an expression of the derived model's quality is improved. Depending on the specific context quality can take several forms and can be related to the generalization performance or to a simple clustering coherence measure. Recently, a novel perspective of semi-supervised learning has been put forward, that associates semi-supervised clustering with the efficiency of spectral methods. More precisely, it has been demonstrated that the appropriate use of partial supervision can bias the data Laplacian matrix such that the necessary eigenvector computations are provably accelerated. This result allows data mining practitioners to use background knowledge not only for improving the quality of clustering results, but also for accelerating the required computations. In this paper we initially provide a high level overview of the relevant efficiency maximizing semi-supervised methods such that their theoretical intuitions are comprehensively outlined. Consecutively, we demonstrate how these methods can be extended to handle multiple clusters and also discuss possible issues that may arise in the continuous semi-supervised solution. Finally, we illustrate the proposed extensions empirically in the context of text clustering.

In some domains, the most appropriate matchings may not strictly preserve ancestry. For instance, while reparenting Tree-matching problems arise in many computational even a single node in a phylogenetic tree of bacteria would domains. The literature provides several destroy its validity, the ancestry relationships in the Document methods for creating correspondences between labeled Object Model tree of a Web page are much less prescriptive: trees; however, by definition, tree-matching moving a search bar from header to footer results in a algorithms rigidly preserve ancestry. That is, once different--but largely equivalent--page. This pattern follows two nodes have been placed in correspondence, for many other tree structures encountered in design and data their descendants must be matched as well. We introduce management, in which hierarchy plays an important--but not flexible tree matching, which relaxes this definitive--role [Chawathe and Garcia-Molina, 1997].

The Semantic Web aims to offer an interoperable environment that will allow users to safely delegate complex actions to intelligent agents. Much work has been done for agents' interoperability; especially in the areas of ontology-based metadata and rule-based reasoning. Nevertheless, the SW proof layer has been neglected so far, although it is vital for agents and humans to understand how a result came about, in order to increase the trust in the interchanged information. This paper focuses on the implementation of third party SW reasoning and proofing services wrapped as agents in a multi-agent framework. This way, agents can exchange and justify their arguments without the need to conform to a common rule paradigm. Via external reasoning and proofing services, the receiving agent can grasp the semantics of the received rule set and check the validity of the inferred results.

The use of ontologies for accessing data is one of the most exciting new applications of description logic in databases and other information systems. A realistic way of realising sufficiently scalable ontology- based data access in practice is by reduction to querying relational databases. In this paper, we describe the ‘combined approach,’ which incorporates the information given by the ontology into the data and employs query rewriting to eliminate spurious answers. We illustrate this approach for ontologies given in the DL-Lite family of description logics and briefly discuss the results obtained for the EL family.

Many complex robot motor skills can be represented using elementary movements, and there exist efficient techniques for learning parametrized motor plans using demonstrations and self-improvement. However with current techniques, in many cases, the robot currently needs to learn a new elementary movement even if a parametrized motor plan exists that covers a related situation. A method is needed that modulates the elementary movement through the meta-parameters of its representation. In this paper, we describe how to learn such mappings from circumstances to meta-parameters using reinforcement learning. In particular we use a kernelized version of the reward-weighted regression. We show two robot applications of the presented setup in robotic domains; the generalization of throwing movements in darts, and of hitting movements in table tennis. We demonstrate that both tasks can be learned successfully using simulated and real robots.

Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users who have direct or indirect social relations with the given user. As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this paper, we explore a model-based approach for recommendation in social networks, employing matrix factorization techniques. Advancing previous work, we incorporate the mechanism of trust propagation into the model in a principled way. Trust propagation has been shown to be a crucial phenomenon in the social sciences, in social network analysis and in trust-based recommendation. We have conducted experiments on two real life data sets. Our experiments demonstrate that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.

There is interest in artificial intelligence for principled techniques to analyze inconsistent information. This stems from the recognition that the dichotomy between consistent and inconsistent sets of formulae that comes from classical logics is not sufficient for describing inconsistent information. We review some existing proposals and make new proposals for measures of inconsistency and measures of information, and then prove that they are all pairwise incompatible. This shows that the notion of inconsistency is a multi-dimensional concept where different measures provide different insights. We then explore relationships between measures of inconsistency and measures of information in terms of the trade-offs they identify when using them to guide resolution of inconsistency.

We show how Finite Model Computation (FMC) of first-order theories can efficiently and transparentlybe solved by taking advantage of an extension of Answer Set Programming, called incremental Answer Set Programming (iASP). The idea is to use the incremental parameter in iASP programs to account for the domain size of a model. The FMC problem is then successively addressed for increasing domain sizes until an answer set, representing a finite model of the original first-order theory, is found. We developed a system based on the iASP solver iClingo and demonstrate its competitiveness.

There is a huge and growing number of systems that depend on batteries for power supply, ranging from small mobile devices to large high-powered systems such as electrical substations. In most of these systems, there are significant user-benefits or engineering reasons to base the supply on multiple batteries, with load being switched between batteries by a control system. The key to efficient use of multiple batteries lies in the design of effective policies for the management of the switching of load between them. This paper describes work in which we show that automated planning can produce much more effective policies than other approaches to multiple battery load management in the literature.

In this paper we present a novel user interface that integrates two popular approaches to language translation for travelers allowing multimodal communication between the parties involved: the picture-book, in which the user simply points to multiple picture icons representing what they want to say, and the statistical machine translation system that can translate arbitrary word sequences. Our prototype system tightly couples both processes within a translation framework that inherits many of the the positive features of both approaches, while at the same time mitigating their main weaknesses. Our system differs from traditional approaches in that its mode of input is a sequence of pictures, rather than text or speech. Text in the source language is generated automatically, and is used as a detailed representation of the intended meaning. The picture sequence which not only provides a rapid method to communicate basic concepts but also gives a `second opinion' on the machine transition output that catches machine translation errors and allows the users to retry the translation, avoiding misunderstandings.