The identity of every human subject is imprinted in the subject’s mind. This work explores one possible approach to authentication of the user identity using implicit long-term memory, specifically, memory for faces.

The rich expressivity provided by the cognitive event calculus (CEC) knowledge representation framework allows for reasoning over deeply nested beliefs, desires, intentions, and so on. I put CEC to the test by attempting to model the complex reasoning and deceptive planning used in an episode of the popular television show Breaking Bad. CEC is used to represent the knowledge used by reasoners coming up with plans like the ones devised by the fictional characters I describe. However, it becomes clear that a form of nonmonotonic reasoning is necessary—specifically so that an agent can reason about the nonmonotonic beliefs of another agent. I show how CEC can be augmented to have this ability, and then provide examples detailing how my proposed augmentation enables much of the reasoning used by agents such as the Breaking Bad characters. I close by discussing what sort of reasoning tool would be necessary to implement such nonmonotonic reasoning.

One of the primary realms into which artificial intelligence research has ventured is that of psychometric tests. It has been debated since Alan Turing proposed the Turing Test whether performance on tests should serve as the metric by which we should determine whether a machine is intelligent. This is an idea that may either solidify or challenge, depending on the reader's predisposition, one's sense of what artificial intelligence really is. As will be discussed in this paper, there is a history of efforts to create agents that perform well on tests in the spirit of an interpretation of artificial intelligence called ``Psychometric AI''. However, the focus of this paper is to describe a machine agent, hereafter called the CFE Agent, developed in this tradition. The CFE Exam is a gateway to certification in the Association of Certified Fraud Examiners (ACFE), a widely recognized professional credential within the fraud examiner profession. The CFE Agent attempts to emulate the successful performance of a human test taker, using what would appear to be simplistic natural language processing approaches to answer test questions. But it is also hoped that the the reader will be convinced that the same core technologies can be successfully applied within the larger domain of fraud detection. Further work will also be briefly discussed, in which we attempt to take these techniques to the next level, a deeper level, by which we can get a better sense of the knowledge the agent is using, and how that knowledge is being applied to formulate answers.

To deceive involves corrupting the predictions or explanations of others. A deeper understanding of how this works thus requires modeling how human abduction and prediction operate. This paper proposes that most human abduction and prediction are carried out via analogy, over experience and generalizations constructed from experience. I take experience to include cultural products, such as stories. How analogical reasoning and learning can be used to make predictions and explanations is outlined, along with both the advantages of this approach and the technical questions that it raises. Concrete examples involving deception and counter-deception are used to explore these ideas further.

An accurate problem list plays the key role of a problem-oriented medical record, which plays a significant role in improving patient care. However, the multi-author, multi-purpose nature of problem list makes it a challenge to maintain, and a single list is difficult, if not impossible, to satisfy all the needs of different practitioners. In this paper, we propose using machine generated problem list to assist a medical practitioner to review a patient’s chart. The proposed system scans both structured and unstructured data in a patient’s electronic medical record (EMR) and generates a ranked, recall-oriented problem list grouped by body systems. Details of each problem are readily available for the user to assess the correctness and relevance of the problem. The user can then provide feedback to the system on the trustworthiness of each evidence passage retrieved, as well as the validity of the problem as a whole. The user-specific feedback provides new information the system needs to perform active learning to learn the user’s preference and produce personalized, and/or domain-specific problem lists.

Timely, relevant, and accurate intelligence analysis is critical to national security, but it is astonishingly complex. This paper provides an intuitive overview of Cogent, a cognitive assistant that facilitates a synergistic integration of analyst's imaginative reasoning with agent's critical reasoning to draw defensible and persuasive conclusions from masses of evidence, in a world that is changing all the time. It presents Cogent's design goals characterizing a new generation of structured analytical tools, introduces the evidence-based analysis concepts on which it is grounded, illustrates a sample session with its current version, and summarizes the cognitive assistance provided to its user.

With advancements in technology, smartphones can already serve as memory aids. Electronic calendars are of great use in time-based memory tasks. In this project, we enter the mixed reality realm for helping dementia patients. Dementia is a general term for a decline in mental ability severe enough to interfere with daily life. Memory loss is an example. Here, mixed reality refers to the merging of real and virtual worlds to produce new episodic memory visualisations where physical and digital objects co-exist and interact in real-time. Cognitive models are approximations of a patient's mental abilities and limitations involving conscious mental activities (such as thinking, understanding, learning, and remembering). External representations of episodic memory help patients and caregivers coordinate their actions with one another. We advocate distributed cognition, which involves the coordination between individuals, artefacts and the environment, in four main implementations of artificial intelligence technology in the Kognit storyboard: (1) speech dialogue and episodic memory retrieval; (2) monitoring medication management and tracking an elder's behaviour (e.g., drinking water); (3) eye tracking and modelling cognitive abilities; and (4) serious game development towards active memory training. We discuss the storyboard, use cases and usage scenarios, and some implementation details of cognitive models and mixed reality hardware for the patient. The purpose of future studies is to determine the extent to which cognitive enhancement technology can be used to decrease caregiver burden.

Exploring web and in particular social media data is an essential task to many of the subject matter experts in order to discover content around their subject of interest. It is important to provide them with a tool to define their scope of vocabulary, i.e what to search for, and suggest them commonly used terms besides the serendipitous terms allowing them to define their scope of explorations. This paper presents methods on constructing ``domain models" which are families of keywords and extractors to enable focus on social media documents relevant to a project using multiple channels of information extraction.

We describe an experiment in using IBM’s Watson cognitive system to teach about human-computer co-creativity in a Georgia Tech Spring 2015 class on computational creativity. The project-based class used Watson to support biologically inspired design, a design paradigm that uses biological systems as analogues for inventing technological systems. The twenty-four students in the class self-organized into six teams of four students each, and developed semester-long projects that built on Watson to support biologically inspired design. In this paper, we describe this experiment in using Watson to teach about human-computer co-creativity, present one project in detail, and summarize the remaining five projects. We also draw lessons on building on Watson for (i) supporting biologically inspired design, and (ii) enhancing human-computer co-creativity.

The updating is accomplished using Bayes'