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 agent 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.
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Inferring and Conveying Intentionality: Beyond Numerical Rewards to Logical Intentions
Shared intentionality is a critical component in developing conscious AI agents capable of collaboration, self-reflection, deliberation, and reasoning. We formulate inference of shared intentionality as an inverse reinforcement learning problem with logical reward specifications.
We show how the approach can infer task descriptions from demonstrations. We also extend our approach to actively convey intentionality. We demonstrate the approach on a simple grid-world example.
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- NSF-PAR ID:
- 10119091
- Date Published:
- Journal Name:
- AAAI Symposium on Towards Conscious AI Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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For holistic accounts of past and present genocides to exist, the production of critical geographies of genocide, produced at the interface of human and physical geography, is vital. The emergent area of critical physical geography (CPG) scholarship stands to provide such holistic accounts in addition to providing genocide studies with a much‐needed geographic perspective, one that is grounded in the lived material realities and social relations produced by the material interactions humans have with their environments. With the land, water, and people of Democratic Kampuchea and contemporary Cambodia as subjects, this work demonstrates how a grounded, empirical CPG approach enhances our understanding of genocide and its aftermath. By examining processes of landscape transformation we suggest that violence, agriculture, and water in the context of Cambodia cannot be considered separate “social” or “natural” components of systems that are either exclusively geopolitical or physiographic. Rather, we emphasise the importance of understanding genocide and violence as internally related to the wider geomorphology—and resulting hydrology and agricultural landscape—produced under the Khmer Rouge. Our approach here has two important consequences. First, the resultant empirical knowledge serves to reinterpret how the Cambodian genocide happened, upending normative myths by demonstrating that the Khmer Rouge consistently acted with intentionality in devaluing human life to the point of mass death while transforming physical landscapes to modernise and enter the global capitalist economy. This reinterpretation serves as a framework for reinterpreting how other genocides may also be stories of continuity and acceleration, rather than aberrant rupture. Second, our CPG approach communicates to genocide studies the need to better situate genocides within their material‐geographic contexts. Specifically, that paying close attention to how humans live and die socio‐politically, relative to the changing physical landscapes around them, can yield significant insights into how and why genocides continue to happen.
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Goal modeling enables the elicitation of stakeholders’ intentionality in the earlier stages of a project. Often, approaches are limited by the effort required to create an initial goal model. In this paper, we investigate the problem of model merging for Tropos goal models. Specifically, we propose a formal approach to the problem of automatically merging the attributes of intentions and actors, once these elements have been matched. Additionally, recent approaches have investigated answering questions about future evolutions of stakeholders’ projects with goal models. In this work we consider both static models, as well as those with timing information, using the principles of gullibility, contradiction, and consensus. We study our implementation and validate the merge operation on a variety of models from the literature.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/inferring intentionality
