In this paper we describe the development and evaluation of AITK, the Artificial Intelligence Toolkit. This open-source project contains both Python libraries and computational essays (Jupyter notebooks) that together are designed to allow a diverse audience with little or no background in AI to interact with a variety of AI tools, exploring in more depth how they function, visualizing their outcomes, and gaining a better understanding of their ethical implications. These notebooks have been piloted at multiple institutions in a variety of humanities courses centered on the theme of responsible AI. In addition, we conducted usability testing of AITK. Our pilot studies and usability testing results indicate that AITK is easy to navigate and effective at helping users gain a better understanding of AI. Our goal, in this time of rapid innovations in AI, is for AITK to provide an accessible resource for faculty from any discipline looking to incorporate AI topics into their courses and for anyone eager to learn more about AI on their own.

Reinforcement Learning (RL) is known to be often unsuccessful in environments with sparse extrinsic rewards. A possible countermeasure is to endow RL agents with an intrinsic reward function, or 'intrinsic motivation', which rewards the agent based on certain features of the current sensor state. An intrinsic reward function based on the principle of empowerment assigns rewards proportional to the amount of control the agent has over its own sensors. We implemented a variation on a recently proposed intrinsically motivated agent, which we refer to as the 'curious' agent, and an empowerment-inspired agent. The former leverages sensor state encoding with a variational autoencoder, while the latter predicts the next sensor state via a variational information bottleneck. We compared the performance of both agents to that of an advantage actor-critic baseline in four sparse reward grid worlds. Both the empowerment agent and its curious competitor seem to benefit to similar extents from their intrinsic rewards. This provides some experimental support to the conjecture that empowerment can be used to drive exploration.

This article introduces Pyro, an open-source Python robotics toolkit for exploring topics in AI and robotics.

This article introduces Pyro, an open-source Python robotics toolkit for exploring topics in AI and robotics. We present key abstractions that allow Pyro controllers to run unchanged on a variety of real and simulated robots. We demonstrate Pyro's use in a set of curricular modules. We then describe how Pyro can provide a smooth transition for the student from symbolic agents to real-world robots, which significantly reduces the cost of learning to use robots. Finally we show how Pyro has been successfully integrated into existing AI and robotics courses.

The Association for the Advancement of Artificial Intelligence presented its 2005 Spring Symposium Series on Monday through Wednesday, March 21-23, 2005 at Stanford University in Stanford, California. The topics of the eight symposia in this symposium series were (1) AI Technologies for Homeland Security; (2) Challenges to Decision Support in a Changing World; (3) Developmental Robotics; (4) Dialogical Robots: Verbal Interaction with Embodied Agents and Situated Devices; (5) Knowledge Collection from Volunteer Contributors; (6) Metacognition in Computation; (7) Persistent Assistants: Living and Working with AI; and (8) Reasoning with Mental and External Diagrams: Computational Modeling and Spatial Assistance.

Techniques in this symposium series were he calls the "twenty-first century for analyzing terrorist networks (1) AI Technologies for Homeland Security; strategic threat triad," which consists were reported by Alphatech (2) Challenges to Decision of failed states, global terrorism, and and the University of Arizona. Popp noted that and retrieving information for Robots: Verbal Interaction with convergence of these three elements counter intelligence was demonstrated Embodied Agents and Situated Devices; is highly destabilizing and a key by Jim Hendler of the University (5) Knowledge Collection from strategic concern to the national security of Maryland. They also aimed to chart out future from Stanford University, Lawrence For example, systems that are research agenda by identifying specific Livermore Laboratories, SRI International, based on probabilistic or decisiontheoretic interesting issues in various and Syracuse University. Homeland security applications for unable to cope with change by themselves, The recurrent themes from data mining and mobile robots were as neither probability theory the presentations included the following: reported by Alphatech and the University nor decision theory says much about of South Florida, respectively. How do The highlights of the symposium let alone how they should be modified.

The Eighth Annual Mobile Robot Competition and Exhibition was held as part of the Sixteenth National Conference on Artificial Intelligence in Orlando, Florida, 18 to 22 July. The goals of these robot events are to foster the sharing of research and technology, allow research groups to showcase their achievements, encourage students to enter robotics and AI fields at both the undergraduate and graduate level, and increase awareness of the field. The 1999 events included two robot contests; a new, long-term robot challenge; an exhibition; and a National Botball Championship for high school teams sponsored by the KISS Institute. Each of these events is described in detail in this article.

The Eighth Annual Mobile Robot Competition and Exhibition was held as part of the Sixteenth National Conference on Artificial Intelligence in Orlando, Florida, 18 to 22 July. The goals of these robot events are to foster the sharing of research and technology, allow research groups to showcase their achievements, encourage students to enter robotics and AI fields at both the undergraduate and graduate level, and increase awareness of the field. The 1999 events included two robot contests; a new, long-term robot challenge; an exhibition; and a National Botball Championship for high school teams sponsored by the KISS Institute. Each of these events is described in detail in this article.

This article posits the idea of robot learning as a new subfield. The results of the Robolearn-96 Workshop provide evidence that learning in modern robotics is distinct from traditional machine learning. The article examines the role of robotics in the social and natural sciences and the potential impact of learning on robotics, generating both a continuum of research issues and a description of the divergent terminology, target domains, and standards of proof associated with robot learning. The article argues that although robot learning is a new subfield, there is significant potential for synergy with traditional machine learning if the differences in research cultures can be overcome.