103 research outputs found
Bayesian Inference of Social Norms as Shared Constraints on Behavior
People act upon their desires, but often, also act in adherence to implicit
social norms. How do people infer these unstated social norms from others'
behavior, especially in novel social contexts? We propose that laypeople have
intuitive theories of social norms as behavioral constraints shared across
different agents in the same social context. We formalize inference of norms
using a Bayesian Theory of Mind approach, and show that this computational
approach provides excellent predictions of how people infer norms in two
scenarios. Our results suggest that people separate the influence of norms and
individual desires on others' actions, and have implications for modelling
generalizations of hidden causes of behavior.Comment: 7 pages, 5 figures, to appear in CogSci 2019, code available at
https://github.com/ztangent/norms-cogsci1
That's Mine! Learning Ownership Relations and Norms for Robots
The ability for autonomous agents to learn and conform to human norms is
crucial for their safety and effectiveness in social environments. While recent
work has led to frameworks for the representation and inference of simple
social rules, research into norm learning remains at an exploratory stage.
Here, we present a robotic system capable of representing, learning, and
inferring ownership relations and norms. Ownership is represented as a graph of
probabilistic relations between objects and their owners, along with a database
of predicate-based norms that constrain the actions permissible on owned
objects. To learn these norms and relations, our system integrates (i) a novel
incremental norm learning algorithm capable of both one-shot learning and
induction from specific examples, (ii) Bayesian inference of ownership
relations in response to apparent rule violations, and (iii) percept-based
prediction of an object's likely owners. Through a series of simulated and
real-world experiments, we demonstrate the competence and flexibility of the
system in performing object manipulation tasks that require a variety of norms
to be followed, laying the groundwork for future research into the acquisition
and application of social norms.Comment: 9 pg., 2 fig., accepted for AAAI-2019. Video demo:
https://bit.ly/2z8obET GitHub: https://github.com/OwnageBot/ownage_bo
Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series
Integrating deep learning with latent state space models has the potential to
yield temporal models that are powerful, yet tractable and interpretable.
Unfortunately, current models are not designed to handle missing data or
multiple data modalities, which are both prevalent in real-world data. In this
work, we introduce a factorized inference method for Multimodal Deep Markov
Models (MDMMs), allowing us to filter and smooth in the presence of missing
data, while also performing uncertainty-aware multimodal fusion. We derive this
method by factorizing the posterior p(z|x) for non-linear state space models,
and develop a variational backward-forward algorithm for inference. Because our
method handles incompleteness over both time and modalities, it is capable of
interpolation, extrapolation, conditional generation, label prediction, and
weakly supervised learning of multimodal time series. We demonstrate these
capabilities on both synthetic and real-world multimodal data under high levels
of data deletion. Our method performs well even with more than 50% missing
data, and outperforms existing deep approaches to inference in latent time
series.Comment: 8 pages, 4 figures, accepted to AAAI 2020, code available at:
https://github.com/ztangent/multimodal-dm
Inferring the Goals of Communicating Agents from Actions and Instructions
When humans cooperate, they frequently coordinate their activity through both
verbal communication and non-verbal actions, using this information to infer a
shared goal and plan. How can we model this inferential ability? In this paper,
we introduce a model of a cooperative team where one agent, the principal, may
communicate natural language instructions about their shared plan to another
agent, the assistant, using GPT-3 as a likelihood function for instruction
utterances. We then show how a third person observer can infer the team's goal
via multi-modal Bayesian inverse planning from actions and instructions,
computing the posterior distribution over goals under the assumption that
agents will act and communicate rationally to achieve them. We evaluate this
approach by comparing it with human goal inferences in a multi-agent gridworld,
finding that our model's inferences closely correlate with human judgments (R =
0.96). When compared to inference from actions alone, we also find that
instructions lead to more rapid and less uncertain goal inference, highlighting
the importance of verbal communication for cooperative agents.Comment: 8 pages, 5 figures. Accepted to the ICML 2023 Workshop on Theory of
Mind in Communicating Agents. Supplementary Information:
https://osf.io/gh758
Cavity magnomechanics: from classical to quantum
Hybrid quantum systems based on magnons in magnetic materials have made
significant progress in the past decade. They are built based on the couplings
of magnons with microwave photons, optical photons, vibration phonons, and
superconducting qubits. In particular, the interactions among magnons,
microwave cavity photons, and vibration phonons form the system of cavity
magnomechanics (CMM), which lies in the interdisciplinary field of cavity QED,
magnonics, quantum optics, and quantum information. Here, we review the
experimental and theoretical progress of this emerging field. We first
introduce the underlying theories of the magnomechanical coupling, and then
some representative classical phenomena that have been experimentally observed,
including magnomechanically induced transparency, magnomechanical dynamical
backactions, magnon-phonon cross-Kerr nonlinearity, etc. We also discuss a
number of theoretical proposals, which show the potential of the CMM system for
preparing different kinds of quantum states of magnons, phonons, and photons,
and hybrid systems combining magnomechanics and optomechanics and relevant
quantum protocols based on them. Finally, we summarize this review and provide
an outlook for the future research directions in this field.Comment: Review article, 42 pages, 16 figure
Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind
When inferring the goals that others are trying to achieve, people
intuitively understand that others might make mistakes along the way. This is
crucial for activities such as teaching, offering assistance, and deciding
between blame or forgiveness. However, Bayesian models of theory of mind have
generally not accounted for these mistakes, instead modeling agents as mostly
optimal in achieving their goals. As a result, they are unable to explain
phenomena like locking oneself out of one's house, or losing a game of chess.
Here, we extend the Bayesian Theory of Mind framework to model boundedly
rational agents who may have mistaken goals, plans, and actions. We formalize
this by modeling agents as probabilistic programs, where goals may be confused
with semantically similar states, plans may be misguided due to
resource-bounded planning, and actions may be unintended due to execution
errors. We present experiments eliciting human goal inferences in two domains:
(i) a gridworld puzzle with gems locked behind doors, and (ii) a block-stacking
domain. Our model better explains human inferences than alternatives, while
generalizing across domains. These findings indicate the importance of modeling
others as bounded agents, in order to account for the full richness of human
intuitive psychology.Comment: Accepted to CogSci 2021. 6 pages, 5 figures. (Appendix: 1 page, 1
figure
The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs
Human beings are social creatures. We routinely reason about other agents,
and a crucial component of this social reasoning is inferring people's goals as
we learn about their actions. In many settings, we can perform intuitive but
reliable goal inference from language descriptions of agents, actions, and the
background environments. In this paper, we study this process of language
driving and influencing social reasoning in a probabilistic goal inference
domain. We propose a neuro-symbolic model that carries out goal inference from
linguistic inputs of agent scenarios. The "neuro" part is a large language
model (LLM) that translates language descriptions to code representations, and
the "symbolic" part is a Bayesian inverse planning engine. To test our model,
we design and run a human experiment on a linguistic goal inference task. Our
model closely matches human response patterns and better predicts human
judgements than using an LLM alone.Comment: To appear at ICML Workshop on Theory of Mind in Communicating Agent
Identification of potential key genes associated with severe pneumonia using mRNA-seq
This study aimed to identify the potential key genes associated with severe pneumonia using mRNA-seq. Nine peripheral blood samples from patients with severe pneumonia alone (SP group, n=3) and severe pneumonia accompanied with chronic obstructive pulmonary disease (COPD; CSP group, n=3), as well as volunteers without pneumonia (control group, n=3) underwent mRNA-seq. Based on the sequencing data, differentially expressed genes (DEGs) were identified by Limma package. Following the pathway enrichment analysis of DEGs, the genes that were differentially expressed in the SP and CSP groups were selected for pathway enrichment analysis and coexpression analysis. In addition, potential genes related to pneumonia were identified based on the information in the Comparative Toxicogenomics Database. In total, 645 and 528 DEGs were identified in the SP and CSP groups, respectively, compared with the normal controls. Among these DEGs, 88 upregulated genes and 80 downregulated genes were common between the two groups. The functions of the common DEGs were similar to those of the DEGs in the SP group. In the coexpression network, the commonly downregulated genes (including ND1, ND3, ND4L, and ND6) and the commonly upregulated genes (including TSPY6P and CDY10P) exhibited a higher degree. In addition, 131 DEGs (including ND1, ND3, ND6, MIR449A and TAS2R43) were predicted to be potential pneumonia-related genes. In conclusion, the present study demonstrated that the common DEGs may be associated with the progression of severe pneumonia
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