Building machines that learn and think with people
What do we want from machine intelligence? We envision machines that are not just tools
for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and …
for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and …
From word models to world models: Translating from natural language to the probabilistic language of thought
How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …
meaning from language--and how can we leverage a theory of linguistic meaning to build …
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning
People often give instructions whose meaning is ambiguous without further context,
expecting that their actions or goals will disambiguate their intentions. How can we build …
expecting that their actions or goals will disambiguate their intentions. How can we build …
When robots get chatty: Grounding multimodal human-robot conversation and collaboration
We investigate the use of Large Language Models (LLMs) to equip neural robotic agents
with human-like social and cognitive competencies, for the purpose of open-ended human …
with human-like social and cognitive competencies, for the purpose of open-ended human …
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 …
communication and non-verbal actions, using this information to infer a shared goal and …
Grounding Language about Belief in a Bayesian Theory-of-Mind
Despite the fact that beliefs are mental states that cannot be directly observed, humans talk
about each others' beliefs on a regular basis, often using rich compositional language to …
about each others' beliefs on a regular basis, often using rich compositional language to …
Understanding Epistemic Language with a Bayesian Theory of Mind
How do people understand and evaluate claims about others' beliefs, even though these
beliefs cannot be directly observed? In this paper, we introduce a cognitive model of …
beliefs cannot be directly observed? In this paper, we introduce a cognitive model of …
GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment
Verbal communication plays a crucial role in human cooperation, particularly when the
partners only have incomplete information about the task, environment, and each other's …
partners only have incomplete information about the task, environment, and each other's …
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends
X Zhang, VS Sheng - arxiv preprint arxiv:2411.04383, 2024 - arxiv.org
Explainability is an essential reason limiting the application of neural networks in many vital
fields. Although neuro-symbolic AI hopes to enhance the overall explainability by leveraging …
fields. Although neuro-symbolic AI hopes to enhance the overall explainability by leveraging …
Bridging the Gap: Representation Spaces in Neuro-Symbolic AI
X Zhang, VS Sheng - arxiv preprint arxiv:2411.04393, 2024 - arxiv.org
Neuro-symbolic AI is an effective method for improving the overall performance of AI models
by combining the advantages of neural networks and symbolic learning. However, there are …
by combining the advantages of neural networks and symbolic learning. However, there are …