Camelot: An Interactive Narrative Sandbox Tool

Camelot is a customizable engine for building simple 3D adventure games, designed for use in interactive narrative research. The engine is controlled by basic text commands sent over standard input, allowing a Camelot game to be controlled by an external program, or experience manager, that can be written in almost any programming language. You can find the latest release and API documentation using the above link.

The project’s design is a collaborative effort by all of us at the Narrative Intelligence Lab. It was built in Unity by Alex Shirvani and Edward Garcia. I wrote the in-house scripting language we use to quickly build Camelot experience managers for testing, demos, and experiments.

Camelot was first showcased as a Playable Experience at AIIDE 2018, and later as its own tutorial workshop at AIIDE 2019: CamJam.

Multi-Agent Narrative Experience Management as Story Graph Pruning

In many intelligent interactive narratives, the player controls an avatar while an experience manager controls non-player characters (NPCs). The space of all stories can be viewed as a story graph, where nodes are states and edges are actions taken by the player, by NPCs, or by both jointly. In this paper, we cast experience management as a story graph pruning problem. We start with the full graph and prune intelligently until each NPC has at most one action in every state. Considering the entire graph allows us to foresee the long-term consequences of every pruning decision on the space of possible stories. By never pruning player actions, we ensure the experience manager can accommodate any choice. When used to control the story of an adventure game, players found our technique generally produced higher agency and more believable NPC behavior than a control.

Full Paper:

PDF

Citation:

Stephen G. Ware, Edward T. Garcia, Alireza Shirvani, Rachelyn Farrell. Multi-agent narrative experience management as story graph pruning. In Proceedings of the 15th AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 87-93, 2019.

[bibtex]

Combining Intentionality and Belief: Revisiting Believable Character Plans

In this paper we present two studies supporting a plan-based model of narrative generation that reasons about both intentionality and belief. First we compare the believability of character plans taken from the spaces of valid classical plans, intentional plans, and belief plans. We show that the plans that make the most sense to humans are those in the overlapping regions of the intentionality and belief spaces. Second, we validate the model’s approach to representing anticipation, where characters form plans that involve actions they expect other characters to take. Using a short interactive scenario we demonstrate that players not only find it believable when NPCs anticipate their actions, but sometimes actively anticipate the actions of NPCs in a way that is consistent with the model.

Full Paper:

PDF

Citation:

Alireza Shirvani, Rachelyn Farrell, Stephen G. Ware. Combining intentionality and belief: revisiting believable character plans. In Proceedings of the 14th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 222-228, 2018.

[bibtex]

A Possible Worlds Model of Belief for State-Space Narrative Planning

Abstract:

What characters believe, how they act based on those beliefs, and how their beliefs are updated is an essential element of many stories. State-space narrative planning algorithms treat their search spaces like a set of temporally possible worlds. We present an extension that models character beliefs as epistemically possible worlds and describe how such a space is generated. We also present the results of an experiment which demonstrates that the model meets the expectations of a human audience.

Full Paper:

PDF

Citation:

Alireza Shirvani, Stephen G. Ware, Rachelyn Farrell. A possible worlds model of belief for state-space narrative planning. In Proceedings of the 13th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 101-107, 2017.

[bibtex]

Fast and Diverse Narrative Planning through Novelty Pruning

Novelty pruning is a simple enhancement that can be added to most planners. A node is removed unless it is possible to find a set of n literals which are true in the current state and have never all been true in any of that plan’s previous states. Expanding on the success of the Iterated Width algorithm in classical planning and general game playing, we apply this technique to narrative planning. Using a suite of 8 benchmark narrative planning problems, we demonstrate that novelty pruning can be used with breadth-first search to solve smaller problems optimally and combined with heuristic search to solve larger problems faster. We also demonstrate that when many solutions to the same problem are generated, novelty pruning can produce a wider variety of solutions in some domains.

Full Paper:

PDF

Citation:

Rachelyn Farrell, Stephen G. Ware. Fast and diverse narrative planning through novelty pruning. In Proceedings of the 12th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 37-43, 2016.

[bibtex]