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Automated extension of narrative planning domains with antonymic operators
AI Planning has been widely used for narrative generation and the control of virtual actors in interactive storytelling. Planning models for such dynamic environments must include alternative actions which enable deviation away from a baseline storyline in order to generate multiple story variants and to be able to respond to changes that might be made to the story world. However, the actual creation of these domain models has been a largely empirical process with a lack of principled approaches to the definition of alternative actions. Our work has addressed this problem and in the paper we present a novel automated method for the generation of interactive narrative domain models from existing non-interactive versions. Central to this is the use of actions that are contrary to those forming the baseline plot within a principled mechanism for their semi-automatic production. It is important that such newly created domain content should still be human-readable and to this end labels for new actions and predicates are generated automatically using antonyms selected from a range of on-line lexical resources. Our approach is fully implemented in a prototype system and its potential demonstrated via both formal experimental evaluation and user evaluation of the generated action labels
Foundations of Human-Aware Planning -- A Tale of Three Models
abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.Dissertation/ThesisDoctoral Dissertation Computer Science 201