Many AI applications rely on knowledge encoded in a locigal knowledge base (KB). The most essential benefit of such logical KBs is the opportunity to perform automatic reasoning which however requires a KB to meet some minimal quality criteria such as consistency. Without adequate tool assistance, the task of resolving such violated quality criteria in a KB can be extremely hard, especially when the problematic KB is large and complex. To this end, interactive KB debuggers have been introduced which ask a user queries whether certain statements must or must not hold in the intended domain. The given answers help to gradually restrict the search space for KB repairs. Existing interactive debuggers often rely on a pool-based strategy for query computation. A pool of query candidates is precomputed, from which the best candidate according to some query quality criterion is selected to be shown to the user. This often leads to the generation of many unnecessary query candidates and thus to a high number of expensive calls to logical reasoning services. We tackle this issue by an in-depth mathematical analysis of diverse real-valued active learning query selection measures in order to determine qualitative criteria that make a query favorable. These criteria are the key to devising efficient heuristic query search methods. The proposed methods enable for the first time a completely reasoner-free query generation for interactive KB debugging while at the same time guaranteeing optimality conditions, e.g. minimal cardinality or best understandability for the user, of the generated query that existing methods cannot realize. Further, we study different relations between active learning measures. The obtained picture gives a hint about which measures are more favorable in which situation or which measures always lead to the same outcomes, based on given types of queries.

When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.

Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this property. For this purpose, we introduce a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space -- or more precisely into an n-dimensional Poincar\'e ball. Due to the underlying hyperbolic geometry, this allows us to learn parsimonious representations of symbolic data by simultaneously capturing hierarchy and similarity. We introduce an efficient algorithm to learn the embeddings based on Riemannian optimization and show experimentally that Poincar\'e embeddings outperform Euclidean embeddings significantly on data with latent hierarchies, both in terms of representation capacity and in terms of generalization ability.

Assessment in intelligent tutoring system (ITS) on concept maps (CM) matches an expert CM to a learner CM. Feedbacks are provided to the learner as semantic comments andvisual corrections. In this paper, quality of feedbacks is improved by using an ontological semantic for matching, formalized as a correlation feedback. Matchings are selected based on an overall assignment solution providing a suboptimal set of correlation feedbacks to the learner.

With the inexorable expansion of the semantic layer on the Web and its ecosystem of connected applications, the global citizens expect more and more data expositions coming from public activities. The recent developments in knowledge representation and reasoning push public structures to deploy their data warehouses in parallel of classical websites exhibitions. This article presents an infrastructure to spread the descriptions of scholarships. After introducing the major contributions concerning the semantical annotation of materials occurring in recruitment processes, we describe our case study about the strategy of the University of Sassari concerning the expositions of academical grants. Supported by a core and aligned ontology of the domain we present our prototypical architecture to support and gather the spread of scholarships.

The idea of dynamic move chains has been described in a preceding paper [10]. Re-using an earlier piece of search allows the tree to be forward-pruned, which is known to be dangerous, because it can potentially remove new information that would only be realised through a more exhaustive search process. The justification is the integrity in the position and small changes between positions make it more likely that an earlier result still applies. Larger problems where exhaustive search is not possible would also like a method that can guess accurately. This paper has added to the forward-pruning technique by using 'move tables' that can act in the same way as Transposition Tables, but for moves not positions. They use an efficient memory structure and have put the design into the context of short or long-term memories. The long-term memory includes simply rote-learning of other players' games. The forward-pruning technique can also be fortified to help to remove some potential errors. Another idea is 'long branches'. This plays a short move sequence, before returning to a full search at the resulting leaf nodes. Therefore, with some configuration the dynamic tables can be reliably used and relatively independently of the position. This has advanced some of the future work theory of the earlier paper, and made more explicit where logical plans and more knowledge-based approaches might be applied. The author would argue that the process is a very human approach to searching for chess moves.

The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining tool-kit ProM (http://promtools.org). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.

House finches based in North American cities and town are better at solving problems than rural ones. Researchers investigated how increased urbanization and human presence affects the behavior and foraging habits of birds. The findings suggests that city birds have become used to humans, but rural birds have not, so they perceive humans as threatening, interfering with their ability to problem solve. The house finch (Haemorhous mexicanus) is a songbird native to the desert areas of North America. It's found in urban and rural areas in Mexico, as well as the southwestern United States.

Steve Miller Band's The Joker was on the FM dial and Mohammed Ali had trounced George Forman to regain his heavyweight title in the much hyped (if questionably named) Rumble in the Jungle. Inflation had reached double digit highs, gasoline was in short supply, President Richard Nixon stepped down in the wake of the Watergate Scandal, and a 29-year-old Hungarian architect by the name of Erno Rubik had finally figured out how he could take a block made of smaller blocks and get the smaller cubes to move without causing the whole structure to fall apart. And thus, the Rubik's cube, which would eventually go on to delight and torment millions, was born. Ostensible a toy, Rubik's cube has since been the subject of everything from high school math class experiments to serious research in computer science. This week, students at the University of Michigan did Rubik one better, building the world's largest freestanding cube and overcoming a design challenge not too dissimilar from Rubik's original quandry--how to get the cubes to spin.

Although an expert Rubik's player can solve the puzzle in seconds, the nature of this particular cube means that even the best player will need a lot more time to finish, according to cube co-developer Ryan Kuhn.