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The semantics of temporal hierarchical planners are limited. In hierarchical paradigms, temporal reasoning has largely focused on durative constraints of primitive actions, which may be added directly or appear post-expansion. We propose extending temporal reasoning to composite actions, specifically within decompositional partial order causal linked planning. We outline how a general-purpose hierarchical planner can approach temporal reasoning outlined in a STRIPS-like for- malism. We build upon existing temporal and hierarchical semantics, and sketch two novel approaches: time-frame planning and decompositional time-frame planning. 

Narrative planning is the use of automated planning to construct, communicate, and understand stories, a form of information to which human cognition and enaction is pre-disposed. We review the narrative planning problem in a manner suitable as an introduction to the area, survey different plan-based methodologies and affordances for reasoning about narrative, and discuss open challenges relevant to the broader AI community. 

Public critiques of technologies and the algorithms that power them have pushed designers to critically consider for whom they design and who they include in design processes. In education, similar critiques highlight how computational technologies designed for novice learners commonly privilege certain ways of knowing and being. In response, this poster explores how the cultural construct of time is represented across computational platforms for novices and what this means, particularly for Indigenous learners and designers. 

To address pressing issues of bias and black boxing embedded in technologies and their underlying computational models, scholars call for inventing and employing design processes that invite participation from those whose lives are shaped by these technologies. In response, we reimagine not only how technologies and their models are designed, but also who designs them. We present our work toward developing the concept of gathering as a design process that invites physical prototyping as an important mechanism in developing culturally sustaining technologies. Gathering is inspired by “Hui,” an ‘Ōlelo Hawai‘i Hawaiian language word translated as: to band together, assemble, organize. We share our ongoing journey of inventing and engaging in gathering and present four characteristics of gathering as a design process. Our work has implications for how we design new forms of technology toward more equitable futures, especially by making visible decision making and sensemaking that occurs throughout the design process. 

Game system models introduce abstractions over games in order to support their analysis, generation, and design. While excellent, models to date leave tacit what they abstract over, why they are ontologically adequate, and how they would be realized in the engine underlying the game. In this paper we model these abstraction gaps via the first-order modal mu-calculus. We use it to reify the link between engines to our game interaction model, a player-computer interaction framework grounded in the Game Ontology Project. Through formal derivation and justification, we contend our work is a useful code studies perspective that affords better understanding the semantics underlying game system models in general. 

We present Bronco: an in-development authoring language for Turing-complete procedural text generation. Our language emerged from a close examination of existing tools. This analysis led to our desire of supporting users in specifying yielding grammars, a formalism we invented that is more expressive than what several popular and available solutions offer. With this formalism as our basis, we detail the qualities of Bronco that expose its power in author-focused ways. 

The study of goal-reasoning agents capable of integrated action and execution has received a great deal of attention in recent years. While practical implementations and theoretical insights of such agents have provided a wealth of flexible behavior in a variety of task environments, they tend to focus on complex environments that are far from classical planning assumptions. This paper formalizes classical planning problems where an agent can change its goal(s) during execution. We identify the minimal changes to classical planning and formalize a model that supports "classical goal reasoning."