This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuS-SIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.

The Rubik's cube was devised by Hungarian architect Erno Rubik more than 30 years ago, but he likely never envisioned his puzzle being cracked this quickly. The machine, known as'Sub1 Reloaded' and developed by German tech company Infineon, was aided by one of the world's most powerful microcomputers, solved a Rubik's cube in 0.637 seconds at the Electronica Trade Fair in Munich, Germany earlier this year. The machine, known as'Sub1 Reloaded' and developed by German tech company Infineon, was aided by one of the world's most powerful microcomputers'Guinness World Records has spent some time carefully reviewing the evidence, including ensuring that the cube and the pre-scrambling met all WCA standards, before confirming the new record today,' the organisation said. The robot took a fraction of a second to analyse the cube and make 21 moves to solve the puzzle. Its time of 0.637 seconds beat the previous world record of 0.887 seconds, set by an earlier prototype of the same machine.

This post is the second in a series of three posts, each of which discuss the fundamental concepts of Artificial Intelligence. In our first post we discussed AI definitions, helping our readers to understand the basic concepts behind AI, giving them the tools required to sift through the many AI articles out there and form their own opinion. In this second post, we will discuss several notions which are important in understanding the limits of AI. Figure 1: How intelligent can Artificial Intelligence get? When we speak about how far AI can go, there are two "philosophies": strong AI and weak AI. The most commonly followed philosophy is that of weak AI, which means that machines can manifest certain intelligent behavior to solve specific (hard) tasks, but that they will never equal the human mind.

Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. Making an approach to pursue the most advanced technology takes a lot of innovation and it is exactly what Mantra Labs has been doing. If you are keen to solve real world problem using AI, Drop us a line hello@mantra.ai

We demonstrate an integrated system for building and learning models and structures in both a real and virtual environment. The system combines natural language understanding, planning, and methods for composition of basic concepts into more complicated concepts. The user and the system interact via natural language to jointly plan and execute tasks involving building structures, with clarifications and demonstrations to teach the system along the way. We use the same architecture for building and simulating models of biology, demonstrating the general-purpose nature of the system where domain-specific knowledge is concentrated in sub-modules with the basic interaction remaining domain-independent. These capabilities are supported by our work on semantic parsing, which generates knowledge structures to be grounded in a physical representation, and composed with existing knowledge to create a dynamic plan for completing goals. Prior work on learning from natural language demonstrations enables learning of models from very few demonstrations, and features are extracted from definitions in natural language. We believe this architecture for interaction opens up a wide possibility of human-computer interaction and knowledge transfer through natural language.

Temporal reasoning is one of the main topics investigated within the field of Artificial Intelligence. Formal methods for temporal reasoning arouse interest of researchers from both theoretical and practical point of view. Such methods enable modelling and studying human-like reasoning mechanisms, thus constituting a valuable tool in cognitive science, philosophy, and linguistics. On the other hand, temporal reasoning formalisms have a number of potential practical applications, e.g., in task scheduling, action planning, and temporal databases. Temporal reasoning methods may be divided into point-based and interval-based depending on the type of the considered primitive ontological objects. My work revolves around the latter type of methods which seem to be more human-like and more suitable for such applications as continuous process modelling. My main result is that the satisfiability problem in a hybridized fragment of Halpern-Shoham logic in which formulas are in a form of conjunction of Horn clauses and only box modal operators are allowed (diamond operators are disallowed) is NP-complete over reflexive, as well as over irreflexive and dense time frames. Before hybridization this fragment was P-complete over such time structures.

This includes memory stores and the representations of elements in those memories, but not their contents, Most research in AI is analytic, in that it selects some facet which change as the result of external stimuli and internal of intelligence and attempts to understand it in detail, typically processing. In this sense, a cognitive architecture is analogous in isolation from other elements. This is balanced by to a building architecture, which describes its fixed a smaller movement, synthetic in character, that aims to discover structure (e.g., floors, rooms, and doors), but not its replaceable how different aspects of intelligence interact.

State estimation is the task of estimating the state of a partially observable dynamical system given a sequence of executed actions and observations. In logical settings, state estimation can be realized via logical filtering, which is exact but can be intractable. We propose logical smoothing, a form of backwards reasoning that works in concert with approximated logical filtering to refine past beliefs in light of new observations.  We characterize the notion of logical smoothing together with an algorithm for backwards-forwards state estimation.  We also present an approximation of our smoothing algorithm that is space efficient. We prove properties of our algorithms, and experimentally demonstrate their behaviour, contrasting them with state estimation methods for planning. Smoothing and backwards-forwards reasoning are important techniques for reasoning about partially observable dynamical systems, introducing the logical analogue of effective techniques from control theory and dynamic programming.

Security problems can be modeled as two-player partially observable stochastic games with one-sided partial observability and infinite horizon (one-sided POSGs). We seek for optimal strategies of player 1 that correspond to robust strategies against the worst-case opponent (player 2) that is assumed to have a perfect information about the game. We present a novel algorithm for approximately solving one-sided POSGs based on the heuristic search value iteration (HSVI) for POMDPs. Our results include (1) theoretical properties of one-sided POSGs and their value functions, (2) guarantees showing the convergence of our algorithm to optimal strategies, and (3) practical demonstration of applicability and scalability of our algorithm on three different domains: pursuit-evasion, patrolling, and search games.

My work on the PhD thesis concerns human-like reasoning about relations between spatial objects and the way they change in time. In particular, my research is focused on logic-based reasoning systems that model human spatial reasoning methods and may enable better understanding of humans reasoning mechanisms in future. Importantly, such formalisms are also interested from the practical point of view – they have a number of potential applications, e.g., in robotics, architecture design, databases, among others.