COKE: A Cognitive Knowledge Graph for Machine Theory of Mind
- URL: http://arxiv.org/abs/2305.05390v2
- Date: Sat, 18 May 2024 10:25:41 GMT
- Title: COKE: A Cognitive Knowledge Graph for Machine Theory of Mind
- Authors: Jincenzi Wu, Zhuang Chen, Jiawen Deng, Sahand Sabour, Helen Meng, Minlie Huang,
- Abstract summary: Theory of mind (ToM) refers to humans' ability to understand and infer the desires, beliefs, and intentions of others.
COKE is the first cognitive knowledge graph for machine theory of mind.
- Score: 87.14703659509502
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Theory of mind (ToM) refers to humans' ability to understand and infer the desires, beliefs, and intentions of others. The acquisition of ToM plays a key role in humans' social cognition and interpersonal relations. Though indispensable for social intelligence, ToM is still lacking for modern AI and NLP systems since they cannot access the human mental state and cognitive process beneath the training corpus. To empower AI systems with the ToM ability and narrow the gap between them and humans, in this paper, we propose COKE: the first cognitive knowledge graph for machine theory of mind. Specifically, COKE formalizes ToM as a collection of 45k+ manually verified cognitive chains that characterize human mental activities and subsequent behavioral/affective responses when facing specific social circumstances. In addition, we further generalize COKE using LLMs and build a powerful generation model COLM tailored for cognitive reasoning. Experimental results in both automatic and human evaluation demonstrate the high quality of COKE, the superior ToM ability of COLM, and its potential to significantly enhance social applications.
Related papers
- Theory of Mind Enhances Collective Intelligence [1.8434042562191815]
We argue that flexible collective intelligence in human social settings is improved by our use of a specific cognitive tool: our Theory of Mind.
We then place these capabilities in the context of the next steps in artificial intelligence embedded in a future that includes an effective human-AI hybrid social ecology.
arXiv Detail & Related papers (2024-11-14T03:58:50Z) - Learning mental states estimation through self-observation: a developmental synergy between intentions and beliefs representations in a deep-learning model of Theory of Mind [0.35154948148425685]
Theory of Mind (ToM) is the ability to attribute beliefs, intentions, or mental states to others.
We show a developmental synergy between learning to predict low-level mental states and attributing high-level ones.
We propose that our computational approach can inform the understanding of human social cognitive development.
arXiv Detail & Related papers (2024-07-25T13:15:25Z) - Spontaneous Theory of Mind for Artificial Intelligence [2.7624021966289605]
We argue for a principled approach to studying and developing AI Theory of Mind (ToM)
We suggest that a robust, or general, ASI will respond to prompts textitand spontaneously engage in social reasoning.
arXiv Detail & Related papers (2024-02-16T22:41:13Z) - On Computational Mechanisms for Shared Intentionality, and Speculation
on Rationality and Consciousness [0.0]
A singular attribute of humankind is our ability to undertake novel, cooperative behavior, or teamwork.
This requires that we can communicate goals, plans, and ideas between the brains of individuals to create shared intentionality.
I derive necessary characteristics of basic mechanisms to enable shared intentionality between prelinguistic computational agents.
arXiv Detail & Related papers (2023-06-03T21:31:38Z) - Machine Psychology [54.287802134327485]
We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology.
We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table.
It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
arXiv Detail & Related papers (2023-03-24T13:24:41Z) - Memory-Augmented Theory of Mind Network [59.9781556714202]
Social reasoning requires the capacity of theory of mind (ToM) to contextualise and attribute mental states to others.
Recent machine learning approaches to ToM have demonstrated that we can train the observer to read the past and present behaviours of other agents.
We tackle the challenges by equipping the observer with novel neural memory mechanisms to encode, and hierarchical attention to selectively retrieve information about others.
This results in ToMMY, a theory of mind model that learns to reason while making little assumptions about the underlying mental processes.
arXiv Detail & Related papers (2023-01-17T14:48:58Z) - Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs [77.88043871260466]
We show that one of today's largest language models lacks this kind of social intelligence out-of-the box.
We conclude that person-centric NLP approaches might be more effective towards neural Theory of Mind.
arXiv Detail & Related papers (2022-10-24T14:58:58Z) - AGENT: A Benchmark for Core Psychological Reasoning [60.35621718321559]
Intuitive psychology is the ability to reason about hidden mental variables that drive observable actions.
Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning.
We present a benchmark consisting of procedurally generated 3D animations, AGENT, structured around four scenarios.
arXiv Detail & Related papers (2021-02-24T14:58:23Z) - Inductive Biases for Deep Learning of Higher-Level Cognition [108.89281493851358]
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles.
This work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing.
The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities.
arXiv Detail & Related papers (2020-11-30T18:29:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.