Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intelligent agents. In this paper, we explore how computational models of analogical processing can be brought into these architectures to enable concept acquisition from examples obtained interactively. We propose a new analogical concept memory for Soar that augments its current system of declarative long-term memories. We frame the problem of concept learning as embedded within the larger context of interactive task learning (ITL) and embodied language processing (ELP). We demonstrate that the analogical learning methods implemented in the proposed memory can quickly learn a diverse types of novel concepts that are useful not only in recognition of a concept in the environment but also in action selection. Our approach has been instantiated in an implemented cognitive system AILEEN and evaluated on a simulated robotic domain.

Mindtree, a global technology services and digital transformation company, announced that it has extended its relationship with premium audio and video solutions brand, EPOS as a digital engineering partner to help augment and accelerate the brand's development of audio technologies and solutions. As part of the multiyear engagement, Mindtree will work as an integrated part of EPOS' development organisation, and take part in strengthening its product innovation, time-to-market, and customer satisfaction, especially in EPOS' high-growth enterprise audio and video segment. Mindtree will provide a broad range of competencies and knowledge within development, maintenance, and quality assurance services to support and innovate all product categories of EPOS within the segments of Enterprise Solutions and Gaming. "This collaboration is important for EPOS to ensure and further develop our portfolio of best-in-class solutions and technologies," said Jeppe Dalberg-Larsen, President at EPOS. "I am confident that Mindtree's extensive product engineering and testing capabilities, coupled with its flexible, transparent, and collaborative approach, will strengthen and support our ability to deliver differentiated audio and video technology, and sound experiences." "We are pleased to partner with an acclaimed audio solutions leader such as EPOS in advancing state-of-the-art digital technologies," said Venu Lambu, Executive Director and President of Global Markets at Mindtree.

The human brain is nothing short of a marvel. We are reminded of this fact each time we read about a technology helping it out of boredom or drawing inspiration from it. How often have you heard complaints about the fact that creativity is being sucked out of human minds when they must do monotonous tasks? Well, Artificial intelligence (AI) and cognitive computing are two stellar technologies crafted in order to reduce human intervention and improve business processes across industries. You needn't be confused about the interchangeable usage, because certain features set AI and cognitive computing apart from each other.

Cognitive computing has been on a lot of minds lately. Looking into the capabilities of Artificial Intelligence to imitate human perception to some extent, the technology innovators have discovered that cognitive computing is a better fit for that. We suddenly realized that a lot more can be done in that regard -- instead of imitating only the perception, we can have technology make decisions like humans. Sharing the idea among the team members of AIHunters, we have tasked ourselves with an ambition of cognitive business automation in the media and entertainment industry. Let us take you on a tour of how we did that -- deliver the solution that puts innovation towards optimizing the video processing and post-production, while pushing beyond the limitations of regular AI analysis.

This paper questions the feasibility of a strong (general) data-centric artificial intelligence (AI). The disadvantages of this type of intelligence are discussed. As an alternative, the concept of co-evolutionary hybrid intelligence is proposed. It is based on the cognitive interoperability of man and machine. An analysis of existing approaches to the construction of cognitive architectures is given. An architecture seamlessly incorporates a human into the loop of intelligent problem solving is considered. The article is organized as follows. The first part contains a critique of data-centric intelligent systems. The reasons why it is impossible to create a strong artificial intelligence based on this type of intelligence are indicated. The second part briefly presents the concept of co-evolutionary hybrid intelligence and shows its advantages. The third part gives an overview and analysis of existing cognitive architectures. It is concluded that many do not consider humans part of the intelligent data processing process. The next part discusses the cognitive architecture for co-evolutionary hybrid intelligence, providing integration with humans. It finishes with general conclusions about the feasibility of developing intelligent systems with humans in the problem-solving loop.

John Laird, the John L. Tishman Professor of Engineering, has been awarded the 2018 Herbert A. Simon Prize for Advances in Cognitive Systems along with his collaborator Prof. Paul Rosenbloom of the University of Southern California. This award recognizes the pair's research on cognitive architectures, especially their Soar project, their applications to knowledge-based systems and models of human cognition, and their contributions to theories of representation, reasoning, problem solving, and learning. The recipients, the awarding committee writes, have been "energetic contributors to AI and cognitive science" for over 30 years. Laird's and Rosenbloom's interdisciplinary and integrative research, both jointly and individually, has addressed many facets of high-level cognition, and their contributions to Soar have helped create one of the industry's most successful tools for developing intelligent systems. Soar is a general cognitive architecture for developing systems that exhibit intelligent behavior.

Our world is being increasingly pervaded by intelligent robots with varying degrees of autonomy. To seamlessly integrate themselves in our society, these machines should possess the ability to navigate the complexities of our daily routines even in the absence of a human's direct input. In other words, we want these robots to understand the intentions of their partners with the purpose of predicting the best way to help them. In this paper, we present CASPER (Cognitive Architecture for Social Perception and Engagement in Robots): a symbolic cognitive architecture that uses qualitative spatial reasoning to anticipate the pursued goal of another agent and to calculate the best collaborative behavior. This is performed through an ensemble of parallel processes that model a low-level action recognition and a high-level goal understanding, both of which are formally verified. We have tested this architecture in a simulated kitchen environment and the results we have collected show that the robot is able to both recognize an ongoing goal and to properly collaborate towards its achievement. This demonstrates a new use of Qualitative Spatial Relations applied to the problem of intention reading in the domain of human-robot interaction.

Welcome to our August 2022 monthly digest, where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. This month, we continue our conference coverage, chat to winners of best paper awards, and listen to some interesting podcasts. Wouldn't it be handy to be able to automatically update information in an outdated article? Well, Robert Logan, Alexandre Passos, Sameer Singh and Ming-Wei Chang designed an algorithm to do just that in their paper FRUIT: Faithfully Reflecting Updated Information in Text. This work won them a best new task award at NAACL 2022 (Annual Conference of the North American Chapter of the Association for Computational Linguistics).

The main goal of cognitive computing is to simulate human thought processes in a computerized model. Using self-learning algorithms that use data mining, pattern recognition, and natural language processing, the computer can mimic the way the human brain works. Furthermore, While computing in Six Industries Inc has been faster at calculations and processing than humans for decades, they haven't been able to accomplish tasks that humans take for granted as simple, like understanding natural language. According to IBM, Watson could eventually be applied in a healthcare setting to help collate the span of knowledge around a condition, including patient history. Furthermore, Journal articles, best practices, and Encryption labs analyze that vast quantity of information and provide a recommendation.

Online autonomous agents are able to draw on a wide variety of potential sources of task knowledge; however current approaches invariably focus on only one or two. Here we investigate the challenges and impact of exploiting diverse knowledge sources to learn, in one-shot, new tasks for a simulated household mobile robot. The resulting agent, developed in the Soar cognitive architecture, uses the following sources of domain and task knowledge: interaction with the environment, task execution and planning knowledge, human natural language instruction, and responses retrieved from a large language model (GPT-3). We explore the distinct contributions of these knowledge sources and evaluate the performance of different combinations in terms of learning correct task knowledge, human workload, and computational costs. The results from combining all sources demonstrate that integration improves one-shot task learning overall in terms of computational costs and human workload.