The manipulation of articulated objects is of primary importance in Robotics, and can be considered as one of the most complex manipulation tasks. Traditionally, this problem has been tackled by developing ad-hoc approaches, which lack flexibility and portability. In this paper we present a framework based on Answer Set Programming (ASP) for the automated manipulation of articulated objects in a robot control architecture. In particular, ASP is employed for representing the configuration of the articulated object, for checking the consistency of such representation in the knowledge base, and for generating the sequence of manipulation actions. The framework is exemplified and validated on the Baxter dual-arm manipulator in a first, simple scenario. Then, we extend such scenario to improve the overall setup accuracy, and to introduce a few constraints in robot actions execution to enforce their feasibility. The extended scenario entails a high number of possible actions that can be fruitfully combined together. Therefore, we exploit macro actions from automated planning in order to provide more effective plans. We validate the overall framework in the extended scenario, thereby confirming the applicability of ASP also in more realistic Robotics settings, and showing the usefulness of macro actions for the robot-based manipulation of articulated objects.

This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC). We present how the representation and inference challenges evolved and the steps which were taken to tackle these challenges. We also present the evaluation methodologies to assess the performance of explainable systems. In addition, we identify persisting open research questions and highlight critical directions for future work.

The difficulty of classical planning increases exponentially with search-tree depth. Heuristic search can make planning more efficient, but good heuristics can be expensive to compute or may require domain-specific information, and such information may not even be available in the more general case of black-box planning. Rather than treating a given planning problem as fixed and carefully constructing a heuristic to match it, we instead rely on the simple and general-purpose "goal-count" heuristic and construct macro-actions to make it more accurate. Our approach searches for macro-actions with focused effects (i.e. macros that modify only a small number of state variables), which align well with the assumptions made by the goal-count heuristic. Our method discovers macros that dramatically improve black-box planning efficiency across a wide range of planning domains, including Rubik's cube, where it generates fewer states than the state-of-the-art LAMA planner with access to the full SAS$^+$ representation.

Computer algorithms are the basic recipes for programming. Professional programmers need to know how to use algorithms to solve difficult programming problems. Written in simple, intuitive English, this book describes how and when to use the most practical classic algorithms, and even how to create new algorithms to meet future needs. The book also includes a collection of questions that can help readers prepare for a programming job interview.

We propose a new class of "grand challenge" AI problems that we call creative captioning---generating clever, interesting, or abstract captions for images, as well as understanding such captions. Creative captioning draws on core AI research areas of vision, natural language processing, narrative reasoning, and social reasoning, and across all these areas, it requires sophisticated uses of common sense and cultural knowledge. In this paper, we analyze several specific research problems that fall under creative captioning, using the popular board game Dixit as both inspiration and proposed testing ground. We expect that Dixit could serve as an engaging and motivating benchmark for creative captioning across numerous AI research communities for the coming 1-2 decades.

Neural Module Network (NMN) is a machine learning model for solving the visual question answering tasks. NMN uses programs to encode modules' structures, and its modularized architecture enables it to solve logical problems more reasonably. However, because of the non-differentiable procedure of module selection, NMN is hard to be trained end-to-end. To overcome this problem, existing work either included ground-truth program into training data or applied reinforcement learning to explore the program. However, both of these methods still have weaknesses. In consideration of this, we proposed a new learning framework for NMN. Graph-based Heuristic Search is the algorithm we proposed to discover the optimal program through a heuristic search on the data structure named Program Graph. Our experiments on FigureQA and CLEVR dataset show that our methods can realize the training of NMN without ground-truth programs and achieve superior efficiency over existing reinforcement learning methods in program exploration.

Natural memories are associative, declarative and distributed. Symbolic computing memories resemble natural memories in their declarative character, and information can be stored and recovered explicitly; however, they lack the associative and distributed properties of natural memories. Sub-symbolic memories developed within the connectionist or artificial neural networks paradigm are associative and distributed, but are unable to express symbolic structure and information cannot be stored and retrieved explicitly; hence, they lack the declarative property. To address this dilemma, we use Relational-Indeterminate Computing to model associative memory registers that hold distributed representations of individual objects. This mode of computing has an intrinsic computing entropy which measures the indeterminacy of representations. This parameter determines the operational characteristics of the memory. Associative registers are embedded in an architecture that maps concrete images expressed in modality-specific buffers into abstract representations, and vice versa, and the memory system as a whole fulfills the three properties of natural memories. The system has been used to model a visual memory holding the representations of hand-written digits, and recognition and recall experiments show that there is a range of entropy values, not too low and not too high, in which associative memory registers have a satisfactory performance. The similarity between the cue and the object recovered in memory retrieve operations depends on the entropy of the memory register holding the representation of the corresponding object. The experiments were implemented in a simulation using a standard computer, but a parallel architecture may be built where the memory operations would take a very reduced number of computing steps.

Researchers at the University of California at Riverside are working to teach computer vision systems what objects typically exist in close proximity to one another so that if one is altered, the system can flag it, potentially thwarting malicious interference with artificial intelligence systems. The yearlong project, supported by a nearly $1 million grant from the Defense Advanced Research Projects Agency, aims to understand how hackers target machine-vision systems with adversarial AI attacks. Led by Amit Roy-Chowdhury, an electrical and computer engineering professor at the school's Marlan and Rosemary Bourns College of Engineering, the project is part of the Machine Vision Disruption program within DARPA's AI Explorations program. Adversarial AI attacks – which attempt to fool machine learning models by supplying deceptive input -- are gaining attention. "Adversarial attacks can destabilize AI technologies, rendering them less safe, predictable, or reliable," Carnegie Mellon University Professor David Danks wrote in IEEE's Spectrum in February.

Japanese toy-maker MegaHouse Corp. said Wednesday it will launch the world's smallest working Rubik's Cube, weighing about 2 grams and measuring 0.99 centimeter on each side. On the same day, the Bandai Namco Holdings Inc. subsidiary started accepting orders for the product online. It is priced at ¥198,000 in Japan, including delivery costs. Delivery will start in late December. The Rubik's Cube, invented by Erno Rubik from Hungary in 1974, hit store shelves across the world in 1980. In Japan, MegaHouse has shipped out over 14 million cubes.

A common issue for companies is that the volume of product orders may at times exceed the production capacity. We formally introduce two novel problems dealing with the question which orders to discard or postpone in order to meet certain (timeliness) goals, and try to approach them by means of model-based diagnosis. In thorough analyses, we identify many similarities of the introduced problems to diagnosis problems, but also reveal crucial idiosyncracies and outline ways to handle or leverage them. Finally, a proof-of-concept evaluation on industrial-scale problem instances from a well-known scheduling benchmark suite demonstrates that one of the two formalized problems can be well attacked by out-of-the-box model-based diagnosis tools.