Multi-agent systems are used in domains where individual component autonomy and cooperation are necessary. The overall system performance requires that the diverse agents maintain quality interactions to facilitate cooperation. A complication to inter-agent interaction occurs when the agents learn (change their own functionality), when new agents are introduced, or existing agents are functionally modified. This research focuses on creating a general use multi-agent system, Middleware Unifying Framework for Independent Nodes System (MUFFINS), and implementing a mechanism, the Megagent, that addresses the interaction challenges. The Megagent provides the ability for agents to assess their performance per data source and to improve it with transformations based on feedback. Evaluation of the concept is tested on data mangled from the Digits dataset to represent learning and new agents and in all cases improves accuracy over a static agent.

When to send system-mediated interruptions within collaborative multi-human-machine environments has been widely debated in the development of interruption management systems. Unfortunately, these studies do not address when to send interruptions in multi-user, multitasking scenarios or predictors of interruptibility within communication tasks. This paper addresses the issue of predicting interruptibility within these interactions with special attention to which users are engaged in which tasks or task engagement and where users are within a current task or task structure as predictors of interruptibility. Using natural human speech from these interactions, we attempt to model task engagement and task structure to predict candidate points of interruptions. The motivation for these models and their performance in a multi-user, multitasking environment are discussed as proposals in developing communication interruption management systems. To model task structure, a task breakpoint model is proposed which performs with a 90% accuracy within a multi-user, multitasking dataset. Integrating this task breakpoint model into a real-time interaction results in an average accuracy of 93% using the proposed task breakpoint model and a rule-based model. To determine the current task in which users are engaged or task engagement, a proposed task topic model performs with an accuracy between 76-88% depending on the topic within the dataset. Closely examining task structure and task engagement as predictors of interruptibility sheds new light on a rarely explored area for system-mediated interruption timings within multi-user, multitasking communication tasks.

Certifiable Trust in Autonomous Systems: Making the Intractable Tangible

This article discusses verification and validation (V&V) of autonomous systems, a concept that will prove to be difficult for systems that were designed to execute decision initiative. V&V of such systems should include evaluations of the trustworthiness of the system based on transparency inputs and scenario-based training. Transparency facets should be used to establish shared awareness and shared intent between the designer, tester, and user of the system. The transparency facets will allow the human to understand the goals, social intent, contextual awareness, task limitations, analytical underpinnings, and team-based orientation of the system in an attempt to verify its trustworthiness. Scenario-based training can then be used to validate that programming in a variety of situations that test the behavioral repertoire of the system. This novel method should be used to analyze behavioral adherence to a set of governing principles coded into the system.

Reports on the 2016 AAAI Fall Symposium Series

The AAAI 2016 Fall Symposium Series was held Thursday through Saturday, November 17–19, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the six symposia were Accelerating Science: A Grand Challenge for AI; Artificial Intelligence for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Cross-Disciplinary Challenges for Autonomous Systems, Privacy and Language Technologies, Shared Autonomy in Research and Practice. The highlights of each (except Acceleration Science) symposium are presented in this report.

The current paper discusses the concept of human-robot interaction through the lens of a model depicting the key elements of robot-to-human and robot-of-human transparency. Robot-to-human factors represent information that the system (which includes the robot but is broader than just the robot) needs to present to users before, during, or after interactions. Robot-of-human variables are factors relating the human (or the interactions with the human; i.e., teamwork) that the system needs to communicate an awareness of to the users. The paper closes with some potentials design implications for the various transparency domains to include: training and the human-robot interface (including social design, feedback, and display design).

According to Baddeley, "The episodic buffer is assumed to be a limitedcapacity Our research team has been working on the development of a language analysis model (Ball, 2011; Ball, Heiberg & temporary storage system that is capable of Silber, 2007) within the ACT-R cognitive architecture integrating information from a variety of sources…the (Anderson, 2007) since 2002 (Ball, 2004). The focus is on buffer provides not only a mechanism for modeling the development of a general-purpose, large-scale, functional environment, but also for creating new cognitive model (Ball, 2008; Ball et al., 2010) that adheres to well representations" (ibid, p. 421). A key empirical result which established cognitive constraints on human language motivated Baddeley to introduce the episodic buffer after 25 processing (HLP) as realized by ACT-R.

The Association for the Advancement of Artificial Intelligence presented the 2007 Fall Symposium Series on Friday through Sunday, November 9–11, at the Westin Arlington Gateway, Arlington, Virginia. The titles of the seven symposia were (1) AI and Consciousness: Theoretical Foundations and Current Approaches, (2) Artificial Intelligence for Prognostics, (3) Cognitive Approaches to Natural Language Processing, (4) Computational Approaches to Representation Change during Learning and Development, (5) Emergent Agents and Socialities: Social and Organizational Aspects of Intelligence, (6) Intelligent Narrative Technologies, and (7) Regarding the "Intelligence" in Distributed Intelligent Systems.

Is it possible to build a conscious machine? There was an almost generally accepted of AI since its beginnings. The symposium was psychological, philosophical, and the first official place where scholars-- neuroscientific theories of consciousness; coming from different fields as far as (3) it is possible to address consciousness neuroscience and philosophy, psychology not only from neuroscience, and computer science--addressed psychology, and philosophy, the issue of consciousness in a but also from AI; and (4) the role of traditional AI environment. Furthermore, embodiment and situatedness is almost there was a good balance of universally recognized. A recurrent topic was the fact that The participants' talks centered on the topic of the symposium and generated the field of consciousness seems to be lively discussions of their research.