Recurrent correlation neural networks (RCNNs), introduced by Chiueh and Goodman as an improved version of the bipolar correlation-based Hopfield neural network, can be used to implement high-capacity associative memories. In this paper, we extend the bipolar RCNNs for processing hypercomplex-valued data. Precisely, we present the mathematical background for a broad class of hypercomplex-valued RCNNs. Then, we provide the necessary conditions which ensure that a hypercomplex-valued RCNN always settles at an equilibrium using either synchronous or asynchronous update modes. Examples with bipolar, complex, hyperbolic, quaternion, and octonion-valued RCNNs are given to illustrate the theoretical results. Finally, computational experiments confirm the potential application of hypercomplex-valued RCNNs as associative memories designed for the storage and recall of gray-scale images.

Vector Symbolic Architectures (VSAs) combine a high-dimensional vector space with a set of carefully designed operators in order to perform symbolic computations with large numerical vectors. Major goals are the exploitation of their representational power and ability to deal with fuzziness and ambiguity. Over the past years, VSAs have been applied to a broad range of tasks and several VSA implementations have been proposed. The available implementations differ in the underlying vector space (e.g., binary vectors or complex-valued vectors) and the particular implementations of the required VSA operators - with important ramifications for the properties of these architectures. For example, not every VSA is equally well suited to address each task, including complete incompatibility. In this paper, we give an overview of eight available VSA implementations and discuss their commonalities and differences in the underlying vector space, bundling, and binding/unbinding operations. We create a taxonomy of available binding/unbinding operations and show an important ramification for non self-inverse binding operation using an example from analogical reasoning. A main contribution is the experimental comparison of the available implementations regarding (1) the capacity of bundles, (2) the approximation quality of non-exact unbinding operations, and (3) the influence of combined binding and bundling operations on the query answering performance. We expect this systematization and comparison to be relevant for development and evaluation of new VSAs, but most importantly, to support the selection of an appropriate VSA for a particular task.

Personality has been identified as a vital factor in understanding the quality of human robot interactions. Despite this the research in this area remains fragmented and lacks a coherent framework. This makes it difficult to understand what we know and identify what we do not. As a result our knowledge of personality in human robot interactions has not kept pace with the deployment of robots in organizations or in our broader society. To address this shortcoming, this paper reviews 83 articles and 84 separate studies to assess the current state of human robot personality research. This review: (1) highlights major thematic research areas, (2) identifies gaps in the literature, (3) derives and presents major conclusions from the literature and (4) offers guidance for future research.

WASHINGTON – The spotlight on brain injuries suffered by American troops in Iraq in January is an example of America's episodic attention to this invisible war wound, which has affected hundreds of thousands over the past two decades but is not yet fully understood. Unlike physical wounds, such as burns or the loss of limbs, traumatic brain injuries aren't obvious and can take time to diagnose. The full impact -- physically and psychologically -- may not be evident for some time, as studies have shown links between TBI and mental health problems. They cannot be dismissed as mere "headaches" -- the word used by President Donald Trump as he said the injuries suffered by the troops in Iraq were not necessarily serious. Gen. Mark Milley, chairman of the Joint Chiefs of Staff and a veteran of combat in Iraq and Afghanistan, told reporters Thursday that the number of service members diagnosed with TBI from the Jan. 8 Iranian missile attack in Iraq has now grown beyond the 50 reported earlier this week, although he provided no specific number.

Deep reinforcement learning (RL) has achieved outstanding results in recent years, which has led a dramatic increase in the number of methods and applications. Recent works are exploring learning beyond single-agent scenarios and considering multi-agent scenarios. However, they are faced with lots of challenges and are seeking for help from traditional game-theoretic algorithms, which, in turn, show bright application promise combined with modern algorithms and boosting computing power. In this survey, we first introduce basic concepts and algorithms in single agent RL and multi-agent systems; then, we summarize the related algorithms from three aspects. Solution concepts from game theory give inspiration to algorithms which try to evaluate the agents or find better solutions in multi-agent systems. Fictitious self-play becomes popular and has a great impact on the algorithm of multi-agent reinforcement learning. Counterfactual regret minimization is an important tool to solve games with incomplete information, and has shown great strength when combined with deep learning.

Developments in Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) currently lead lively debates in academia and practice. AI processes data to perform tasks that were previously thought possible only for humans to perform. DLT acts in uncertain environments to create consensus over data among a group of participants. In recent articles, both technologies complement each other. Examples include the design of secure distributed ledgers or the creation of allied learning systems distributed across multiple nodes. This can lead to technological convergence, which in the past, has paved the way for major IT product innovations. Previous work highlights several potential benefits of the convergence of AI and DLT but only provides a limited theoretical framework to describe upcoming real-world integration cases of both technologies. We aim to contribute by conducting a systematic literature review on the previous work and by providing rigorously derived future research opportunities. Our analysis identifies how AI and DLT exchange data, and how to use these integration principles to build new systems. Based on that, we present open questions for future research. This work helps researchers active in AI or DLT to overcome current limitations in their field, and engineers to develop systems along with the convergence of these technologies.

Conformance checking techniques are widely adopted to pinpoint possible discrepancies between process models and the execution of the process in reality. However, state of the art approaches adopt a crisp evaluation of deviations, with the result that small violations are considered at the same level of significant ones. This affects the quality of the provided diagnostics, especially when there exists some tolerance with respect to reasonably small violations, and hampers the flexibility of the process. In this work, we propose a novel approach which allows to represent actors' tolerance with respect to violations and to account for severity of deviations when assessing executions compliance. We argue that besides improving the quality of the provided diagnostics, allowing some tolerance in deviations assessment also enhances the flexibility of conformance checking techniques and, indirectly, paves the way for improving the resilience of the overall process management system.

Brain-Computer Interface (BCI) is a powerful communication tool between users and systems, which enhances the capability of the human brain in communicating and interacting with the environment directly. Advances in neuroscience and computer science in the past decades have led to exciting developments in BCI, thereby making BCI a top interdisciplinary research area in computational neuroscience and intelligence. Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications. Many people benefit from EEG-based BCIs, which facilitate continuous monitoring of fluctuations in cognitive states under monotonous tasks in the workplace or at home. In this study, we survey the recent literature of EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensated for the gaps in the systematic summary of the past five years (2015-2019). In specific, we first review the current status of BCI and its significant obstacles. Then, we present advanced signal sensing and enhancement technologies to collect and clean EEG signals, respectively. Furthermore, we demonstrate state-of-art computational intelligence techniques, including interpretable fuzzy models, transfer learning, deep learning, and combinations, to monitor, maintain, or track human cognitive states and operating performance in prevalent applications. Finally, we deliver a couple of innovative BCI-inspired healthcare applications and discuss some future research directions in EEG-based BCIs.

There is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embedding (KGE) models have been proposed that learn low dimensional representations while trying to maintain the structural properties of the KGs such as the similarity of nodes depending on their edges to other nodes. KGEs can be used to address tasks within KGs such as the prediction of novel links and the disambiguation of entities. They can also be used for downstream tasks like question answering and fact-checking. Overall, these tasks are relevant for the semantic web community. Despite their popularity, the reproducibility of KGE experiments and the transferability of proposed KGE models to research fields outside the machine learning community can be a major challenge. Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability. The KEEN Universe currently consists of the Python packages PyKEEN (Python KnowlEdge EmbeddiNgs), BioKEEN (Biological KnowlEdge EmbeddiNgs), and the KEEN Model Zoo for sharing trained KGE models with the community.

With all the buzz in the information technology industry around artificial intelligence (AI) and machine learning (ML) you'd think that every organization was using these tools or planning for how they are going to use them. After all, the promise is that AI and ML will help organizations harness the ever-growing volumes of data being generated by automating and augmenting human analytic processes and decision-making.