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This paper addresses the role of conversational memory in Embodied Conversational Agents (ECAs). It describes an investigation into developing such a memory architecture and integrating it into an ECA. ECAs are virtual agents whose purpose is to engage in conversations with human users, typically through natural language speech. While several works in the literature seek to produce viable ECA dialog architectures, only a few authors have addressed the episodic memory architectures in conversational agents and their role in enhancing their intelligence. In this work, we propose, implement, and test a unified episodic memory architecture for ECAs. We describe a process that determines the prevalent contexts in the conversations obtained from the interactions. The process presented demonstrates the use of multiple techniques to extract and store relevant snippets from long conversations, most of whose contents are unremarkable and need not be remembered. The mechanisms used to store, retrieve, and recall episodes from previous conversations are presented and discussed. Finally, we test our episodic memory architecture to assess its effectiveness. The results indicate moderate success in some aspects of the memory-enhanced ECAs, as well as some work still to be done in other aspects.

This article describes and evaluates an approach to create and/or improve tactical agents through direct human interaction in real time through a force-feedback haptic device. This concept takes advantage of a force-feedback joystick to enhance motor skill and decision-making transfer from the human to the agent in real time. Haptic devices have been shown to have high bandwidth and sensitivity. Experiments are described for this new approach, named Instructional Learning. It is used both as a way to build agents from scratch as well as to improve and/or correct agents built through other means. The approach is evaluated through experiments that involve three applications of increasing complexity – chasing a fleer (Chaser), shepherding a flock of sheep into a pen (Sheep), and driving a virtual automobile (Car) through a simulated road network. The results indicate that in some instances, instructional learning can successfully create agents under some circumstances. However, instructional learning failed to build and/or improve agents in other instances. The Instructional Learning approach, the experiments, and their results are described and extensively discussed.

This article describes research to build an embodied conversational agent (ECA) as an interface to a question-and-answer (Q/A) system about a National Science Foundation (NSF) program. We call this ECA the LifeLike Avatar, and it can interact with its users in spoken natural language to answer general as well as specific questions about specific topics. In an idealized case, the LifeLike Avatar could conceivably provide a user with a level of interaction such that he or she would not be certain as to whether he or she is talking to the actual person via video teleconference. This could be considered a (vastly) extended version of the seminal Turing test. Although passing such a test is still far off, our work moves the science in that direction. The Uncanny Valley notwithstanding, applications of such lifelike interfaces could include those where specific instructors/caregivers could be represented as stand-ins for the actual person in situations where personal representation is important. Possible areas that come to mind that might benefit from these lifelike ECAs include health-care support for elderly/disabled patients in extended home care, education/training, and knowledge preservation. Another more personal application would be to posthumously preserve elements of the persona of a loved one by family members. We apply this approach to a Q/A system for knowledge preservation and dissemination, where the specific individual who had this knowledge was to retire from the US National Science Foundation. The system is described in detail, and evaluations were performed to determine how well the system was perceived by users.

This paper describes an interactive museum exhibit featuring an avatar of Alan Turing that informs museum visitors about artificial intelligence and Turing's seminal Turing Test for machine intelligence. The objective of the exhibit is to engage and motivate visiting children in the hope of sparking an interest in them about computer science and artificial intelligence, and cause them to consider pursuing future studies and/or careers in these fields. The exhibit interacts with the visitors, allowing them to participate in a simplified version of Turing's test that is brief and informal to suit the limitations of a five-minute exhibit. In this exhibit, the visitor (targeted towards middle school age children) invokes an avatar of his/her own choice, and acts to endow it with human-like qualities (voice, brain, eyesight and hearing). Then, the visitor engages the avatar in a (brief) question-and-answer session to determine whether the visitor thinks that he/she is interacting with a real human on a video conference or with an avatar. We consider this interaction to be an extension of the original Turing Test because, unlike Turing's original that used text via a teletype, this version features a graphical embodiment of an agent with which one can converse in spoken natural language. This extension serves to make passing the Turing Test more difficult, as now the avatar must not only communicate like a human, but also look, sound and act the part. It also makes the exhibit visual, dynamic and interesting to the visitors. Evaluations were performed with museum visitors, both in backrooms with prototypes as well as on the museum floor with the final version. The formative and summative evaluations performed indicated overall success in engaging the museum visitors and increasing their interest in computer science. More specifically, the formative testing, mostly done in quiet back rooms with selected test subjects, indicated that on the important questions about enjoyment of exhibit and increased interest in computer science by the test subjects, their self-reported Likert scale responses (1 being negative and 5 being positive) increased from 3.16 in the first evaluation to 4.38 in the third one for increased interest in CS. Likewise for the question about exhibit enjoyment (from 3.92 to 4.56). The summative evaluation, done through unobtrusive observation of exhibit use on museum floor, indicated that almost 74% of the parties that initiated the exhibit were either highly or moderately engaged by the exhibit. However, there was one major negative finding, namely the overly long duration of the exhibit, which may have caused premature abandonment of the exhibit in several cases during the summative evaluation. These tests and their results are presented and discussed in detail in this paper. The exhibit has been on permanent display at the Orlando (FL) Science Center since June 2014 and has received a strongly positive response from visitors since that time.

This paper presents a review of research works from the last several years in automated story generation systems. These systems are categorised into interactive story generation systems and non-Interactive story generation systems. Interactive systems are those that collaborate with a user/author during the process of creating and/or executing the story. The extent of user interaction varies across systems but remains an integral part of the creation and/or the unfolding of the story. Non-Interactive systems concentrate on complete automation of the creative process involved in narrative generation to create diverse and interesting stories. Interactive story generators specifically designed for video game narratives are reviewed as a separate sub-class of interactive story generation systems. Also reviewed are the methods used for evaluation of story generation systems as a way to explore the possibility of having standard methods of evaluation within the research community. The paper includes a discussion of trends and directions of the research discipline. [ABSTRACT FROM AUTHOR]

This paper explores the issues faced in creating a system that can learn tactical human behavior merely by observing a human perform the behavior in a simulation. More specifically, this paper describes a technique based on fuzzy ARTMAP (FAM) neural networks to discover the criteria that cause a transition between contexts during a strategic game simulation. The approach depends on existing context templates that can identify the high-level action of the human, given a description of the situation along with his action. The learning task then becomes the identification and representation of the context sequence executed by the human. In this paper, we present the FAM/Template-based Interpretation Learning Engine (FAMTILE). This system seeks to achieve this learning task by constructing rules that govern the context transitions made by the human. To evaluate FAMTILE, six test scenarios were developed to achieve three distinct evaluation goals: 1) to assess the learning capabilities of FAM; 2) to evaluate the ability of FAMTILE to correctly predict human and context selections, given an observation; and 3) more fundamentally, to create a model of the human's behavior that can perform the high-level task at a comparable level of proficiency. Index Terms--Context-Based Reasoning (CxBR), fuzzy ARTMAP (FAM), learning from observation, neural network, poker, template-based interpretation (TBI).

Human experts employed in validation exercises for knowledge-based systems (KBSs) often have limited time and availability. Furthermore, they often have different opinions from each other as well as from themselves over time. We address this situation by introducing the use of validation knowledge used in prior validation exercises for the same KBS. We present a validation knowledge base (VKB) that is the collective best experience of several human experts. The VKB is constructed and maintained across various validation exercises, and its primary benefits are given as follows: 1) more reliable validation results by incorporating external knowledge and 2) decrease of the experts' workload. We also present the concept of validation expert software agents (VESAs), which represent a particular expert's knowledge. VESA is a software agent corresponding to a specific human expert. It models the validation knowledge and behavior of its human counterpart by analyzing similarities with the responses of other experts. After a learning period, it can be used to temporarily substitute for its corresponding human expert. We also describe experiments with a small prototype system to evaluate the usefulness of these concepts. Index Terms--Knowledge-based systems (KBSs), systems, validation knowledge, validation of artificial intelligence (AI) systems.