Search for: All records

This paper describes the ways in which an established K12 informal learning program, called Young Women in Computing (YWIC), utilizes culturally sustaining pedagogical practices to support learning, development, and leadership of youth outreach participants as well as undergraduate instructional staff. Authors emphasize the leadership roles undergraduates (here, authors 1-3) play in developing and implementing outreach designed and embodied at a Hispanic[1]Serving Institution. The three themes illustrated in this study include (1) opportunities for agency, or ownership, choice and autonomy for undergraduate leaders, (2) an emphasis on relationality, or developing personal relationships among undergraduate leaders and youth, and (3) the multiplicity of relevant knowledge and “ways of knowing” which contribute to viable pathways into computing. This paper argues the elevation of undergraduates better apprentices the next diverse educators and leaders in computing. 

In multi-agent domains (MADs), an agent's action may not just change the world and the agent's knowledge and beliefs about the world, but also may change other agents' knowledge and beliefs about the world and their knowledge and beliefs about other agents' knowledge and beliefs about the world. The goals of an agent in a multi-agent world may involve manipulating the knowledge and beliefs of other agents' and again, not just their knowledge/belief about the world, but also their knowledge about other agents' knowledge about the world. Our goal is to present an action language (mA+) that has the necessary features to address the above aspects in representing and RAC in MADs. mA+ allows the representation of and reasoning about different types of actions that an agent can perform in a domain where many other agents might be present -- such as world-altering actions, sensing actions, and announcement/communication actions. It also allows the specification of agents' dynamic awareness of action occurrences which has future implications on what agents' know about the world and other agents' knowledge about the world. mA+ considers three different types of awareness: full-, partial- awareness, and complete oblivion of an action occurrence and its effects. This keeps the language simple, yet powerful enough to address a large variety of knowledge manipulation scenarios in MADs. The semantics of mA+ relies on the notion of state, which is described by a pointed Kripke model and is used to encode the agent's knowledge and the real state of the world. It is defined by a transition function that maps pairs of actions and states into sets of states. We illustrate properties of the action theories, including properties that guarantee finiteness of the set of initial states and their practical implementability. Finally, we relate mA+ to other related formalisms that contribute to RAC in MADs.