Stephen Muggleton gave the invited talk "Meta-Interpretive Inductive Logic Programming (ILP) is a research area Learning: achievements and challenges". Meta-Interpretive formed at the intersection of Machine Learning and logicbased Learning (MIL) is an ILP technique aimed at supporting knowledge representation. ILP has originally used learning of recursive definitions, by automatically introducing logic programming as a uniform representation language sub-definitions that allow decomposition into a hierarchy for examples, background knowledge and hypotheses for of reusable parts (Muggleton et al. 2014; 2015). ILP has also explored several connections (or abducing) first-order clauses whose heads unify with with statistical learning and other probabilistic approaches, a given goal. MIL additionally fetches higher-order metarules expanding research horizons significantly. A recent survey whose heads unify with the goal and saves the resulting of ILP can be seen in (Muggleton et al. 2012).

In this abstract, we first consider abduction in human dialogues. Two different types of abduction, objective abduction and subjective abduction, are introduced and formulated using propositional modal logic. We next formulate conversational implicature in the same logic and contrast it with abduction in dialogues. According to our formulation, abduction uses private belief of a reasoner, while conversational implicature relies on common knowledge between participants in conversation. The results characterize how hearers use abduction or conversational implicatures to figure out what speakers have implicated and show how two commonsense inferences are distinguished.

If we already know a particular concept representations by considering the semantic relations between such as pets, we can describe a new concept such as dogs words. Specifically, given as input a relational graph, by stating the semantic relations that the new concept shares a directed labelled weighted graph where vertices represent with the existing concepts such as dogs belongs-to pets. Alternatively, words and edges represent numerous semantic relations we could describe a novel concept by listing all that exist between the corresponding words, we consider the the attributes it shares with existing concepts. In our example, problem of learning a vector representation for each vertex we can describe the concept dog by listing attributes (word) in the graph and a matrix representation for each label such as mammal, carnivorous, and domestic animal that it type (pattern). The learnt word representations are evaluated shares with another concept such as the cat. Therefore, both for their accuracy by using them to solve semantic word attributes and relations can be considered as alternative descriptors analogy questions on a benchmark dataset. of the same knowledge. This close connection between Our task of learning word attributes using relations between attributes and relations can be seen in knowledge representation words is challenging because of several reasons. First, schemes such as predicate logic, where attributes there can be multiple semantic relations between two words.

In this game, each player deal with enormous sizes such as a smart grid is rapidly increasing associated with a variable keeps providing probability distributions in AI communities. The distributed constraint optimization over its domain, and tries to minimize the regret, problem (DCOP for short) is arguably the most which is the average additional cost incurred by the probability studied problem in this setting, where the goal is to find an distributions against the strategy of outputting a best assignment that minimizes the total sum of costs incurred single value all the time. We can make the regret of each by (local) cost functions. Since it takes a prohibitively long agent arbitrarily small by utilizing the multiplicative weights time to exactly solve DCOP, we need to resort to incomplete method. Finally, we round the obtained probability distributions algorithms, and a plethora of incomplete algorithms to integer values. We prove that our method converges have been proposed in the literature, such as local search to a certain kind of equilibrium, called a coarse correlated based algorithms (Maheswaran, Pearce, and Tambe 2004; equilibrium. Zhang et al. 2005), inference based algorithms (Farinelli We empirically compare our methods with previous stateof-the-art et al. 2008), graph based algorithms (Bowring et al. 2008; methods. We demonstrate that our methods are Kiekintveld et al. 2010), divide-and-coordinate based algorithms scalable, and that DMW-Game outperforms other methods (Vinyals, Rodriguez-Aguilar, and Cerquides 2010; in terms of solution quality and efficiency. Hatano and Hirayama 2013), and sampling based algorithms (Ottens, Dimitrakakis, and Faltings 2012; Nguyen, Yeoh, and Lau 2013).

Belief revision games (BRGs) are concerned with the dynamics of the beliefs of a group of communicating agents. BRGs are "zero-player" games where at each step every agent revises her own beliefs by taking account for the beliefs of her acquaintances. Each agent is associated with a belief state defined on some finite propositional language. We provide a general definition for such games where each agent has her own revision policy, and show that the belief sequences of agents can always be finitely characterized. We then define a set of revision policies based on belief merging operators. We point out a set of appealing properties for BRGs and investigate the extent to which these properties are satisfied by the merging-based policies under consideration.

We propose an indexing scheme for top-k shortest-path distance queries on graphs, which is useful in a wide range of important applications such as network-aware search and link prediction. While considerable effort has been made for efficiently answering standard (top-1) distance queries, none of previous methods can be directly extended for top-k distance queries. We propose a new framework for top-k distance queries based on 2-hop cover and then present an efficient indexing algorithm based on the simple but effective recent notion of pruned landmark labeling. Extensive experimental results on real social and web graphs show the scalability, efficiency and robustness of our method. Moreover, we demonstrate the usefulness of top-k distance queries through an application to link prediction.

In this paper, we formulate a new problem related to the well-known influence maximization in the context of computational advertising. Our new problem considers allocating marketing channels (e.g., TV, newspaper, and websites) to advertisers from the view point of a match maker, which was not taken into account in previous studies on the influence maximization. The objective of the problem is to find an allocation such that each advertiser can influence some given number of customers while the slots of marketing channels are limited. We propose an algorithm based on the Lagrangian decomposition. We empirically show that our algorithm computes better quality solutions than existing algorithms, scales up to graphs of 10M vertices, and performs well particularly in a parallel environment.

Crowdsourcing is a technique to outsource tasks to a number of workers. Although crowdsourcing has many advantages, it gives rise to the risk that sensitive information may be leaked, which has limited the spread of its popularity. Task instances (data workers receive to process tasks) often contain sensitive information, which can be extracted by workers. For example, in an audio transcription task, an audio file corresponds to an instance, and the content of the audio (e.g., the abstract of a meeting) can be sensitive information. In this paper, we propose a quantitative analysis framework for the instance privacy problem. The proposed framework supplies us performance measures of instance privacy preserving protocols. As a case study, we apply the proposed framework to an instance clipping protocol and analyze the properties of the protocol. The protocol preserves privacy by clipping instances to limit the amount of information workers obtain. The results show that the protocol can balance task performance and instance privacy preservation. They also show that the proposed measure is consistent with standard measures, which validates the proposed measure.

Quality control is one of the central issues in crowdsourcing research. In this paper, we consider a quality control problem of crowdsourced enumeration tasks that request workers to enumerate possible answers as many as possible. Since workers neither necessarily provide correct answers nor provide exactly the same answers even if the answers indicate the same idea, we propose a two-stage quality control method consisting of the answer clustering stage and the reliability estimation stage.

Predictive modeling competitions provide a new data mining approach that leverages crowds of data scientists to examine a wide variety of predictive models and build the best performance model. In this paper, we report the results of a study conducted on CrowdSolving, a platform for predictive modeling competitions in Japan. We hosted a competition on a link prediction task and observed that (i) the prediction performance of the winner significantly outperformed that of a state-of-the-art method, (ii) the aggregated model constructed from all submitted models further improved the final performance, and (iii) the performance of the aggregated model built only from early submissions nevertheless overtook the final performance of the winner.