Author name cluster

Adam Amos-Binks

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

PRL Workshop 2021 Workshop Paper

dcss ai wrapper: An API for Dungeon Crawl Stone Soup providing both Vector and Symbolic State Representations

Dustin Dannenhauer
Zohreh A. Dannenhauer
Jonathon Decker
Adam Amos-Binks
Michael Floyd
David Aha

Dungeon Crawl Stone Soup is a single-player, free, and opensource rogue-like video game with a variety of features that make it a challenge for artificial intelligence (AI) research. dcss-ai-wrapper is the first API designed to enable intelligent agents to play Dungeon Crawl Stone Soup. We describe the vector and symbolic relational state representations available through the dcss-ai-wrapper, as well as how to use the API to develop custom agents. By providing both vector and relational representations, we hope to spur advances in reinforcement learning, automated planning, and other cognitive and learning techniques. This API is similar in spirit to recent game APIs such as the Nethack Learning Environment, MALMO, ELF, and the Starcraft II API. The complexities of Dungeon Crawl Stone Soup include actions with delayed consequences, partial observability, stochastic actions where probabilities change over time, extremely sparse rewards, procedurally generated environments, sensing actions, and dynamic monsters and level-specific events. Our contributions are (1) a description of the publicly available dcssai-wrapper, (2) an API that supports both vector and PDDL representations of the DCSS game state, and (3) a high-level PDDL model of Dungeon Crawl Stone Soup compatible with the FastDownward planner. dcss-ai-wrapper is available at https: //github. com/dtdannen/dcss-ai-wrapper.

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AAAI Conference 2018 Short Paper

Plan-Based Intention Revision

Adam Amos-Binks
R. Young

Plan-based story generation has operationalized concepts from the Belief-Desire-Intention (BDI) theory of mind to create goal-driven character agents with explainable behavior. However, these character agents are limited in that they do not capture the dynamic nature of intentions. To address this limitation, we define a plan-based intention revision model and propose an evaluation using the QUEST cognitive model to assess the explainability of an intention revision.

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AAAI Conference 2017 Short Paper

Problem Formulation for Accommodation Support in Plan-Based Interactive Narratives

Adam Amos-Binks

Branching story games have gained popularity for adapting to user actions within a story world. An active area of Interactive Narrative (IN) research uses automated planning to generate story plans as it can lighten the authorial burden of writing a branching story. Branches can be generated from a declarative representation rather than hand-crafted. A goal of an Experience Manager (EM) is to guide a user through a space of desirable narrative trajectories, or story branches, in an IN. However, in the cases when an EM must accommodate user actions and mediate them from a desired narrative trajectory to a new narrative trajectory, automated planningÕs authorial advantage becomes a liability as the available narrative trajectories are not known apriori. This limitation can lead to the EM choosing a new narrative trajectory that is not coherent with the previous one and may result in a negative user experience. The goal of my research is to develop a problem formulation methodology for story planning problems that elicits the available narrative trajectories enabling an EM to execute more coherent accommodations.

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