12 research outputs found
Leveraging Contextual Counterfactuals Toward Belief Calibration
Beliefs and values are increasingly being incorporated into our AI systems
through alignment processes, such as carefully curating data collection
principles or regularizing the loss function used for training. However, the
meta-alignment problem is that these human beliefs are diverse and not aligned
across populations; furthermore, the implicit strength of each belief may not
be well calibrated even among humans, especially when trying to generalize
across contexts. Specifically, in high regret situations, we observe that
contextual counterfactuals and recourse costs are particularly important in
updating a decision maker's beliefs and the strengths to which such beliefs are
held. Therefore, we argue that including counterfactuals is key to an accurate
calibration of beliefs during alignment. To do this, we first segment belief
diversity into two categories: subjectivity (across individuals within a
population) and epistemic uncertainty (within an individual across different
contexts). By leveraging our notion of epistemic uncertainty, we introduce `the
belief calibration cycle' framework to more holistically calibrate this
diversity of beliefs with context-driven counterfactual reasoning by using a
multi-objective optimization. We empirically apply our framework for finding a
Pareto frontier of clustered optimal belief strengths that generalize across
different contexts, demonstrating its efficacy on a toy dataset for credit
decisions.Comment: ICML (International Conference on Machine Learning) Workshop on
Counterfactuals in Minds and Machines, 202
Getting aligned on representational alignment
Biological and artificial information processing systems form representations
that they can use to categorize, reason, plan, navigate, and make decisions.
How can we measure the extent to which the representations formed by these
diverse systems agree? Do similarities in representations then translate into
similar behavior? How can a system's representations be modified to better
match those of another system? These questions pertaining to the study of
representational alignment are at the heart of some of the most active research
areas in cognitive science, neuroscience, and machine learning. For example,
cognitive scientists measure the representational alignment of multiple
individuals to identify shared cognitive priors, neuroscientists align fMRI
responses from multiple individuals into a shared representational space for
group-level analyses, and ML researchers distill knowledge from teacher models
into student models by increasing their alignment. Unfortunately, there is
limited knowledge transfer between research communities interested in
representational alignment, so progress in one field often ends up being
rediscovered independently in another. Thus, greater cross-field communication
would be advantageous. To improve communication between these fields, we
propose a unifying framework that can serve as a common language between
researchers studying representational alignment. We survey the literature from
all three fields and demonstrate how prior work fits into this framework.
Finally, we lay out open problems in representational alignment where progress
can benefit all three of these fields. We hope that our work can catalyze
cross-disciplinary collaboration and accelerate progress for all communities
studying and developing information processing systems. We note that this is a
working paper and encourage readers to reach out with their suggestions for
future revisions.Comment: Working paper, changes to be made in upcoming revision
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Authorial Leverage: Artificial Intelligence for Narrative and Storytelling
Intelligent Narrative Technologies have changed storytelling by facilitating new types of story experiences, such as interactive stories. However, Intelligent Narrative Technologies also introduce a prohibitively high cost of authorship, which goes beyond the effort it takes to learn how to use new technology and tools. New technology replaces aspects of traditional human authoring with instructions and procedures. Evaluating the power of expressiveness in different storytelling approaches is similar to understanding the difference in utility between a hammer and a nail gun, or between Python and Assembly.This dissertation asks why such authorship is so difficult, and proposes a technique for making it easier. The three main contributions of this work are: (1) it provides an author-centric design model for intelligent narrative, positioning storytelling as the management of variations; (2) it introduces the Authorial Leverage (AL) framework, a means for evaluating the costs and benefits of interactive story authorship; and (3) it introduces a system designed to demonstrate the aforementioned concepts.The author-centric design model sees interactive stories as composed of constituent and supplementary events, as opposed to the traditional story-and- discourse model. This allows us to build interactive storytelling tools with greater authorial leverage. We describe one such system, called RoleModel
Recommended from our members
Authorial Leverage: Artificial Intelligence for Narrative and Storytelling
Intelligent Narrative Technologies have changed storytelling by facilitating new types of story experiences, such as interactive stories. However, Intelligent Narrative Technologies also introduce a prohibitively high cost of authorship, which goes beyond the effort it takes to learn how to use new technology and tools. New technology replaces aspects of traditional human authoring with instructions and procedures. Evaluating the power of expressiveness in different storytelling approaches is similar to understanding the difference in utility between a hammer and a nail gun, or between Python and Assembly.This dissertation asks why such authorship is so difficult, and proposes a technique for making it easier. The three main contributions of this work are: (1) it provides an author-centric design model for intelligent narrative, positioning storytelling as the management of variations; (2) it introduces the Authorial Leverage (AL) framework, a means for evaluating the costs and benefits of interactive story authorship; and (3) it introduces a system designed to demonstrate the aforementioned concepts.The author-centric design model sees interactive stories as composed of constituent and supplementary events, as opposed to the traditional story-and- discourse model. This allows us to build interactive storytelling tools with greater authorial leverage. We describe one such system, called RoleModel
Towards Mixed-Initiative Generation of Multi-Channel Sequential Structure
We argue for the benefit of designing deep generative models through a mixed-initiative, co-creative combination of deep learning algorithms and human specifications, focusing on multi-channel music composition. Sequence models have shown convincing results in domains such as summarization and translation; however, longer-term structure remains a major challenge. Given lengthy inputs and outputs, deep generative systems still lack reliable representations of beginnings, middles, and ends, which are standard aspects of creating content in domains such as music composition. This paper aims to contribute a framework for mixed-initiative generation approaches that let humans both supply and control some of these aspects in deep generative models for music, and present a case study of Counterpoint by Convolutional Neural Network (CoCoNet)
Integrating Drama Management into an Adventure Game
Often, video game designers must choose between creating a linear experience, and designing an open world with many different story lines that fail to form a tightly crafted narrative arc. A drama manager (DM) can provide a solution to this dilemma. ADM monitors an interactive experience, such as a computer game, and intervenes to shape the global experience so that it satisfies the author's expressive goals without decreasing a player's interactive agency. In this demo we present the first integration of declarative optimization-based drama management (DODM) into an adventure-style dungeon game called EMPath
Evaluating the Authorial Leverage of Drama Management
A drama manager (DM) monitors an interactive experience, such as a computer game, and intervenes to shape the global experience so that it satisfies the author’s expressive goals without decreasing a player’s interactive agency. Most research on drama management has proposed AI architectures and provided abstract evaluations of their effectiveness; a smaller body of work has also evaluated the effect of drama management on player experience. Little attention has been paid, however, to evaluating the authorial leverage provided by a drama-management architecture: determining, for a given architecture, the additional non-linear story complexity a drama manager affords over traditional scripting methods. In this paper, we propose three criteria for evaluating the authorial leverage of a DM: (1) the script-and-trigger complexity of the DM story policy; (2) the degree of policy change given changes to story elements; and (3) the average story branching factor for DM policies versus script-and-trigger policies for stories of equivalent quality. We apply these criteria to declarative optimization-based drama management (DODM) by using decision tree learning to capture equivalent trigger logic, and show that DODM does in fact provide authorial leverag