Interest in teaching-track faculty positions has been steadily increasing as enrollments in computer science degree programs continue to trend upward. While departments have welcomed these new teaching-track faculty members, senior faculty, department chairs, and university committees often struggle with how to best evaluate these faculty members during the promotion process. In our experience, some universities try to use a "watered-down" version of the tenure-track promotion standards with the intent of uniformity. Other universities have created whole new processes, which may be better at capturing the differences in teaching-track positions, but also can create a "second-class citizen" status for the teaching-track faculty members.

As teaching-focused positions proliferate and university teaching careers become more professionalized, there is growing attention being paid to how teaching faculty are created. However, how teaching faculty are hired also deserves scrutiny.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of ten AI assignments from the 2019 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http: //modelai.gettysburg.edu.

KEOD 2017 - 9th International Conference on Knowledge Engineering and Ontology Development

Service learning offers students of computer science an experiential opportunity to hone not only their technical skills of design and programming, but also the soft skills of teamwork, communication, and social/ethical behavior. With hard work and effective mentoring from faculty, the output of student work can also benefit community partners, assuming there is proper infrastructure in place to provide long-term project management and technical support. This Birds of a Feather session provides a discussion platform to share lessons learned and best practices related to establishing a sustainable service-learning program within an undergraduate computer-science curriculum, with a particular focus on balancing benefits to students and community partners with organizational overhead.

Human Computer Interaction (HCI) is central for many applications, including hazardous environment inspection and telemedicine. Whereas traditional methods ofHCI for teleoperating electromechanical systems include joysticks, levers, or buttons, our research focuses on using electromyography (EMG) signals to improve intuition and response time. An important challenge is to accurately and efficiently extract and map EMG signals to known position for real-time control. In this preliminary work, we compare the accuracy and real-time performance of several machine-learning techniques for recognizing specific arm positions. We present results from offline analysis, as well as end-to-end operation using a robotic arm.

We address the problem of planning collision-free paths for multiple agents using optimization methods known as proximal algorithms. Recently this approach was explored in Bento et al. (2013), which demonstrated its ease of parallelization and decentralization, the speed with which the algorithms generate good quality solutions, and its ability to incorporate different proximal operators, each ensuring that paths satisfy a desired property. Unfortunately, the operators derived only apply to paths in 2D and require that any intermediate waypoints we might want agents to follow be preassigned to specific agents, limiting their range of applicability. In this paper we resolve these limitations. We introduce new operators to deal with agents moving in arbitrary dimensions that are faster to compute than their 2D predecessors and we introduce landmarks, space-time positions that are automatically assigned to the set of agents under different optimality criteria. Finally, we report the performance of the new operators in several numerical experiments.

We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning. The al- gorithm builds a forest of trees whose nodes store previ- ously seen examples. It can be shown data points one at a time and updates itself incrementally, hence it is nat- urally online. Few instance-based algorithms have this property while being simultaneously fast, which the BF is. This is crucial for applications where one needs to respond to input data in real time. The number of chil- dren of each node is not set beforehand but obtained from the training procedure, which makes the algorithm very flexible with regards to what data manifolds it can learn. We test its generalization performance and speed on a range of benchmark datasets and detail in which settings it outperforms the state of the art. Empirically we find that training time scales as O(DN log(N )) and testing as O(Dlog(N)), where D is the dimensionality and N the amount of data.

We describe a novel approach for computing collision-free \emph{global} trajectories for $p$ agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM) algorithm. Compared with existing methods, our approach is naturally parallelizable and allows for incorporating different cost functionals with only minor adjustments. We apply our method to classical challenging instances and observe that its computational requirements scale well with $p$ for several cost functionals. We also show that a specialization of our algorithm can be used for {\em local} motion planning by solving the problem of joint optimization in velocity space.

Episodic memory endows agents with numerous general cognitive capabilities, such as action modeling and virtual sensing. However, for long-lived agents, there are numerous unexplored computational challenges in supporting useful episodic-memory functions while maintaining real-time reactivity. In this paper, we review the implementation of episodic memory in Soar and present an expansive evaluation of that system. We demonstrate useful applications of episodic memory across a variety of domains, including games, mobile robotics, planning, and linguistics. In these domains, we characterize properties of environments, tasks, and episodic cues that affect performance, and evaluate the ability of Soar’s episodic memory to support hours to days of real-time operation.

One issue facing agents that accumulate large bodies of knowledge is determining whether they have knowl- edge that is relevant to its current goals. Performing comprehensive searches of long-term memory in every situation can be computationally expensive and disrup- tive to task reasoning. In this paper, we demonstrate that the recognition judgment — a heuristic for whether memory structures have been previously perceived — can serve as a low-cost indicator of the existence of potentially relevant knowledge. We present an approach for computing both context-dependent and context- independent recognition judgments using processes and data shared with declarative memories. We then de- scribe an initial, efficient implementation in the Soar cognitive architecture and evaluate our system in a word sense disambiguation task, showing that it reduces the number of memory searches without degrading agent performance.

Effective access to knowledge within large declarative memory stores is one challenge in the development and understanding of long-living, generally intelligent agents. We focus on a sub-component of this problem: given a large store of knowledge, how should an agent's task-independent memory mechanism respond to an ambiguous cue, one that pertains to multiple previously encoded memories. A large body of cognitive modeling work suggests that human memory retrievals are biased in part by the recency and frequency of past memory access. In this paper, we evaluate the functional benefit of a set of memory retrieval heuristics that incorporate these biases, in the context of the word sense disambiguation task, in which an agent must identify the most appropriate word meaning in response to an ambiguous linguistic cue. In addition, we develop methods to integrate these retrieval biases within a task-independent declarative memory system implemented in the Soar cognitive architecture and evaluate their effectiveness and efficiency in three commonly used semantic concordances.