Hybrid reasoners combine multiple types of reasoning, usually subsumption and Prolog-style resolution. We outline a system which combines natural deduction and subsumption reasoning using Inference Graphs implementing a Logic of Arbitrary and Indefinite Objects.

With the rapid growth of event-based social networks, the demand of event recommendation becomes increasingly important. Different from classic recommendation problems, event recommendation generally faces the problems of heterogenous online and offline social relationships among users and implicit feedback data. In this paper, we present a baysian probability model that can fully unleash the power of heterogenous social relations and efficiently tackle with implicit feedback characteristic for event recommendation. Experimental results on several real-world datasets demonstrate the utility of our method.

The knowledgebase uncertainty and the argument preferences are considered in this paper. The uncertainty is captured by weighted satisfiability degree, while a preference relation over arguments is derived by the beliefs of an agent.

Reputation models depend on the ratings provided by buyers togauge the reliability of sellers in multi-agent based e-commerce environment. However, there is no prevention forthe cases in which a buyer misjudges a seller, and provides a negative rating to an original satisfactory transaction. In this case,how should the seller get his reputation repaired andutility loss recovered? In this work, we propose a mechanism to mitigate the negativeeffect of the misreported ratings. It temporarily inflates the reputation of thevictim seller with a certain value for a period of time. This allows the seller to recover hisutility loss due to lost opportunities caused by the misreported ratings. Experiments demonstrate the necessity and effectiveness of the proposed mechanism.

Users of a social network like to follow the posts published by influential users. Such posts usually are delivered quickly and thus will produce a strong influence on public opinions. In this paper, we focus on the problem of identifying domain-dependent influential users(or topic experts). Some of traditional approaches are based on the post contents of users user’s to identify influential users, which may be biased by spammers who try to make posts related to some topics through a simple copy and paste. Others make use of user authentication information given by a service platform or user self description (introduction or label) in finding influential users. However, what users have published is not necessarily related to what they have registed and described. In addition, if there is no comments from other users, it’s less objective to assess a user’s post quality. To improve effectiveness of recognizing influential users in a topic of microblogs, we propose a post-feature based approach which is supplementary to post-content based approaches. Our experimental results show that the post-feature based approach produces relatively higher precision than that of the content based approach.

One challenge of link prediction in online social networks is the large scale of many such networks. The measures used by existing work lack a computational consideration in the large scale setting. We propose the notion of social distance in a multi-dimensional form to measure the closeness among a group of people in Microblogs. We proposed a fast hashing approach called Locality-sensitive Social Distance Hashing (LSDH), which works in an unsupervised setup and performs approximate near neighbor search without high-dimensional distance computation. Experiments were applied over a Twitter dataset and the preliminary results testified the effectiveness of LSDH in predicting the likelihood of future associations between people.

Humorous images can be seen in many social media websites. However, newcomers to these websites often have trouble fitting in because of the subculture among the community is usually implicit. Among all the types of humorous images, Internet memes are relatively hard for newcomers to understand. In this work, we develop a system leveraging crowdsourcing technique to generate explanations for meme images. We claim that people who are not familiar with Internet meme subculture can still quickly pick up the gist of the memes through reading the explanations. Our template-based explanation can illustrate the incongruity between normal situations and the punchlines in jokes. The explanations can be produced by going through 2 designed humor tasks. In our pilot study, acceptable explanations for 5 unique memes are generated. For further study, generating explanations for more general text jokes are possible.

Keepaway soccer is a challenging robot control task that has been widely used as a benchmark for evaluating multi-agent learning systems. The majority of research in this domain has been from the perspective of reinforcement learning (function approximation) and neuroevolution. One of the challenges under multi-agent tasks such as keepaway is to formulate effective mechanisms for diversity maintenance. Indeed the best results to date on this task utilize some form of neuroevolution with genotypic diversity. In this work, a symbiotic framework for evolving teams of programs is utilized with both genotypic and behavioural forms of diversity maintenance considered. Specific contributions of this work include a simple scheme for characterizing genotypic diversity under teams of programs and its comparison to behavioural formulations for diversity under the keepaway soccer task. Unlike previous research concerning diversity maintenance in genetic programming (GP), we are explicitly interested in solutions taking the form of teams of programs.

As a promising alternative to using standard (often intractable) planning techniques with Bellman equations, we propose an interesting method of optimizing POMDP controllers by probabilistic inference in a novel equivalent single-DBN generative model. Our inference approach to POMDP planning allows for (1) for application of various techniques for probabilistic inference in single graphical models, and (2) for exploiting the factored structure in a controller architecture to take advantage of natural structural constrains of planning problems and represent them compactly. Our contributions can be summarized as follows: (1) we designed a novel single-DBN generative model that ensures that the task of probabilistic inference is equivalent to the original problem of optimizing POMDP controllers, and (2) we developed several inference approaches to approximate the value of the policy when exact inference methods are not tractable to solve large-size problems with complex graphical models. The proposed approaches to policy optimization by probabilistic inference are evaluated on several POMDP benchmark problems and the performance of the implemented approximation algorithms is compared.

This Our primary contribution is the development of an algorithm costs valuable time for finding and rescuing survivors, so that uses a small set of distinct messages but still sending in an advance team of robots to scout the environment achieves full exploration using a robot team that is too small and locate points of interest can save time by assisting to achieve blanket coverage of the environment.