It is not uncommon to find a tool needed for a certain task unavailable. However, humans tend to circumvent such hurdle by improvising the usability of a suitable existing object in the environment. For a robot who is expected to work alongside humans in the real word is bound to face such obstacles and an effective way to carry on with the task for it would be to find a substitute. Robots that, for instance, have to hammer a nail into a wall should look for a conventional tool, a hammer, or resort to an appropriate substitute in case a hammer is unavailable. A selection of an appropriate substitute requires a knowledge driven deliberation to determine its suitability. Baber in Baber (2003a) suggested that humans are aided by conceptual knowledge about objects during the deliberation process. In other terms, humans generally have an intuitive understanding of objects and as such use qualitative form of knowledge about properties of objects - thus, conceptual knowledge - obtained from a combination of visual sensations, experiences and the outcomes of manual investigation to evaluate the applicability of a substitute.

Data streams occur widely in various real world applications. The research on streaming data mainly focuses on the data management, query evaluation and optimization on these data, however the work on reasoning procedures for streaming knowledge bases on both the assertional and terminological levels is very limited. Typically reasoning services on large knowledge bases are very expensive, and need to be applied continuously when the data is received as a stream. Hence new techniques for optimizing this continuous process is needed for developing efficient reasoners on streaming data. In this paper, we survey the related research on reasoning on expressive logics that can be applied to this setting, and point to further research directions in this area.

As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. In this paper, we aim to present a survey to comprehensively introduce the current techniques proposed on this topic. Specifically, current SSL techniques can be mainly divided into two categories. The first category of SSL approaches can be called "concept learning", which emphasizes learning new concepts from only few related observations. The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis. The second category is called "experience learning", which usually co-exists with the large sample learning manner of conventional machine learning. This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures. More extensive surveys on both categories of SSL techniques are introduced and some neuroscience evidences are provided to clarify the rationality of the entire SSL regime, and the relationship with human learning process. Some discussions on the main challenges and possible future research directions along this line are also presented.

One of the world's oldest and most popular toys is getting a face-lift. Israel-based startup Particula has unveiled its spin on the Rubik's cube, dubbed the GoCube, that can connect to your phone and enables users to play against other people. It marks a major step up from when the original Rubik's cube, developed by Hungarian sculptor Erno Rubik, was first released in 1974. Since then, over 350 million Rubik's cubes have been sold worldwide. Israel-based startup Particula has unveiled its spin on the Rubik's cube, dubbed the GoCube (pictured), that can connect to your phone and enables users to play against other people The GoCube syncs up with smartphones and tablets using a Bluetooth connection, giving users access to the Battle feature, which lets them'play friends (or enemies) across the world, according to Particula.

Working memory is an essential component of reasoning -- the capacity to answer a new question by manipulating acquired knowledge. Current memory-augmented neural networks offer a differentiable method to realize limited reasoning with support of a working memory module. Memory modules are often implemented as a set of memory slots without explicit relational exchange of content. This does not naturally match multi-relational domains in which data is structured. We design a new model dubbed Relational Dynamic Memory Network (RDMN) to fill this gap. The memory can have a single or multiple components, each of which realizes a multi-relational graph of memory slots. The memory is dynamically updated in the reasoning process controlled by the central controller. We evaluate the capability of RDMN on several important application domains: software vulnerability, molecular bioactivity and chemical reaction. Results demonstrate the efficacy of the proposed model.

Rule-based temporal query languages provide the expressive power and flexibility required to capture in a natural way complex analysis tasks over streaming data. Stream processing applications, however, typically require near real-time response using limited resources. In particular, it becomes essential that the underpinning query language has favourable computational properties and that stream processing algorithms are able to keep only a small number of previously received facts in memory at any point in time without sacrificing correctness. In this paper, we propose a recursive fragment of temporal Datalog with tractable data complexity and study the properties of a generic stream reasoning algorithm for this fragment. We focus on the window validity problem as a way to minimise the number of time points for which the stream reasoning algorithm needs to keep data in memory at any point in time.

This paper describes a novel approach to software engineering derived from the "SP Theory of Intelligence" and its realisation in the "SP Computer Model". Despite superficial appearances, it is shown that many of the key ideas in software engineering have counterparts in the structure and workings of the SP system. Potential benefits of this new approach to software engineering include: the automation or semi-automation of software development, with support for programming of the SP system where necessary; allowing programmers to concentrate on 'world-oriented' parallelism, without worries about parallelism to speed up processing; support for the long-term goal of programming the SP system via written or spoken natural language; reducing or eliminating the distinction between 'design' and 'implementation'; reducing or eliminating operations like compiling or interpretation; reducing or eliminating the need for verification of software; reducing the need for validation of software; no formal distinction between program and database; the potential for substantial reductions in the number of types of data file and the number of computer languages; benefits for version control; and reducing technical debt.

This paper describes a roadmap for the development of the "SP Machine", based on the "SP Theory of Intelligence" and its realisation in the "SP Computer Model". The SP Machine will be developed initially as a software virtual machine with high levels of parallel processing, hosted on a high-performance computer. The system should help users visualise knowledge structures and processing. Research is needed into how the system may discover low-level features in speech and in images. Strengths of the SP system in the processing of natural language may be augmented, in conjunction with the further development of the SP system's strengths in unsupervised learning. Strengths of the SP system in pattern recognition may be developed for computer vision. Work is needed on the representation of numbers and the performance of arithmetic processes. A computer model is needed of "SP-Neural", the version of the SP Theory expressed in terms of neurons and their inter-connections. The SP Machine has potential in many areas of application, several of which may be realised on short-to-medium timescales.

Answer Set Programming (ASP) is a well-known declarative problem solving approach based on nonmonotonic logic programs, which has been successfully applied to a wide range of applications in artificial intelligence and beyond. To address the needs of modern applications, HEX-programs were introduced as an extension of ASP with external atoms for accessing information outside programs via an API style bi-directional interface mechanism. To evaluate such programs, conflict-driving learning algorithms for SAT and ASP solving have been extended in order to capture the semantics of external atoms. However, a drawback of the state-of-the-art approach is that external atoms are only evaluated under complete assignments (i.e., input to the external source) while in practice, their values often can be determined already based on partial assignments alone (i.e., from incomplete input to the external source). This prevents early backtracking in case of conflicts, and hinders more efficient evaluation of HEX-programs. We thus extend the notion of external atoms to allow for three-valued evaluation under partial assignments, while the two-valued semantics of the overall HEX-formalism remains unchanged. This paves the way for three enhancements: first, to evaluate external sources at any point during model search, which can trigger learning knowledge about the source behavior and/or early backtracking in the spirit of theory propagation in SAT modulo theories (SMT). Second, to optimize the knowledge learned in terms of so-called nogoods, which roughly speaking are impossible input-output configurations. Shrinking nogoods to their relevant input part leads to more effective search space pruning. And third, to make a necessary minimality check of candidate answer sets more efficient by exploiting early external evaluation calls. As this check usually accounts for a large share of the total runtime, optimization is here particularly important. We further present an experimental evaluation of an implementation of a novel HEX-algorithm that incorporates these enhancements using a benchmark suite. Our results demonstrate a clear efficiency gain over the state-of-the-art HEX-solver for the benchmarks, and provide insights regarding the most effective combinations of solver configurations.

Syntactic search relies on keywords contained in a query to find suitable documents. So, documents that do not contain the keywords but contain information related to the query are not retrieved. Spreading activation is an algorithm for finding latent information in a query by exploiting relations between nodes in an associative network or semantic network. However, the classical spreading activation algorithm uses all relations of a node in the network that will add unsuitable information into the query. In this paper, we propose a novel approach for semantic text search, called query-oriented-constrained spreading activation that only uses relations relating to the content of the query to find really related information. Experiments on a benchmark dataset show that, in terms of the MAP measure, our search engine is 18.9% and 43.8% respectively better than the syntactic search and the search using the classical constrained spreading activation. NTRODUCTION With rapid development of the Word Wide Web and e-societies, information retrieval (IR) has many challenges in exploiting those rich and huge information resources. Whereas, the keyword based IR has many limitations in finding suitable documents for user's queries. Semantic search improves search precision and recall by understanding user's intent and the contextual meaning of terms in documents and queries.