In collabroation with Dr. Sarah Harmon at Bowdoin College, we will be presenting work on the design of a board game that explicitly includes vocabulary from conflict resolution work in Social Sciences. A draft of the paper is available here [TTG_FDG_Conflict_StarStruck] The paper is going to be presented at the Tabletop Games Workshop at the Foundations of Digital Games Conference.
Operationalizing Conflict Strategies in a Board Game
The aim of conflict resolution education is to impart essential strategies and skills for resolving conflicts effectively. While these are important life skills, conflict resolution can be difficult to teach because it requires individuals to interact with others, explore new strategies, and receive feedback within a natural social context in order for strong connections to be made. As board games often involve co-located multiplayer interaction and can be effective tools for young learners, we explore the possibility of incorporating learning about conflict resolution into a tabletop game. We describe the process of designing an educational board game — StarStruck — that fosters discussions about conflict management via operationalization of conflict strategies drawn from an instrument founded in social psychology theory. Through in- and out-of-board interactions, StarStruck is designed to provide players with affordances to explore different resolution strategies within their natural social environment. We present examples from initial playtesting sessions to consider the expressive range of conflict scenarios generated by playing the game. This work serves as a preliminary illustration of how to map the vocabulary of conflict resolution to game mechanics, dynamics, and aesthetics so that players can naturally engage with and discuss conflict interactions.
In collaboration with the POEM lab, we will be presenting work on procedural narrative generation at the Procedural Content Generation workshop at the 2019 Foundations of Digital Games Conference. Here is a draft of the full paper : [FDG_PCG_Workshop_Paper: Stories of the Town]
Stories of the Town: Balancing Character Autonomy and Coherent Narrative in Procedurally Generated Worlds
Procedural narrative generation systems often focus on autonomous agent based simulations to create emergent interactions, plan-based approaches to provide guarantees for coherence, or using elements of simulation to guide plan-based approaches. These different approaches, with some exceptions, tend to trade off character autonomy in service of more designer controlled experiences or content authoring in service of encoding domain knowledge of possible branches of the narrative and participating characters. We have developed a system, called Stories of the Town, that automatically generates narratives by synthesizing three distinct approaches to traditional narrative generation: context-free grammars, planning, and simulation.
More specifically, our system generates narratives via probabilistic context-free grammars applied to state-space planning problem solutions from planning problem formulations of simulated character models. Our system uses character simulations to generate variety in narratives and ensures narrative coherence through authoring probabilistic context-free grammars. By doing so, this system takes advantage of the strengths of each individual approach (e.g. controllability, scalability, intentionality, and variety) to generate narratives that are extensible, expressive, consistent with simulated character personalities and histories, and controllable. We show that this system has strong potential in automatically generating varied, complex, consistent, and goal-oriented narratives. Further development of the system will allow for more efficient utilization of the strengths of each narrative generation approach while also using these strengths to supplement their individual shortcomings.
Lab’s paper led by Morteza Behrooz tiled Story Quality as a Matter of Perception: Using Word Embeddings to Estimate Cognitive Interest has been accepted for ORAL presentation at the AIIDE conference 2019 to be held in Atlanta, GA from October 8-12.
Storytelling is a capable tool for interactive agents and better stories can enable better interactions. Many existing automated evaluation techniques are either focused on textual features that are not necessarily reflective of perceived interestingness (e.g. coherence), or are domain-specific, relying on a priori semantics models (e.g. in a game). However, the effectiveness of storytelling depends both on its versatility to adapt to new domains and the perceived interestingness of its generated stories. In this paper, drawing from cognitive science literature, we propose and evaluate a method for estimating cognitive interest in stories based on the level of predictive inference they cause during perception.
This paper continues the thread of determining measures of interestingness in mundane event sequences when they are communicated as narratives.
Investigating the Use of Word Embeddings to Estimate Cognitive Interest in Stories, proceedings of the Annual Conference of the Cognitive Science Society, 2019 (forthcoming)
Cognitive and Experiential Interest in Abstract Visual Narrative, proceedings of the Annual Conference of the Cognitive Science Society, 2018
Modeling Social Interestingness in Conversational Stories, in proceedings of the Australasian Computer Science Week Multi-Conference, 2018.