Metacognition is all you need? Using Introspection in Generative Agents
to Improve Goal-directed Behavior
- URL: http://arxiv.org/abs/2401.10910v2
- Date: Thu, 29 Feb 2024 21:05:00 GMT
- Title: Metacognition is all you need? Using Introspection in Generative Agents
to Improve Goal-directed Behavior
- Authors: Jason Toy, Josh MacAdam, Phil Tabor
- Abstract summary: We introduce a metacognition module for generative agents, enabling them to observe their own thought processes and actions.
We tested the metacognition module on a variety of scenarios, including a situation where generative agents must survive a zombie apocalypse.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in Large Language Models (LLMs) have shown impressive
capabilities in various applications, yet LLMs face challenges such as limited
context windows and difficulties in generalization. In this paper, we introduce
a metacognition module for generative agents, enabling them to observe their
own thought processes and actions. This metacognitive approach, designed to
emulate System 1 and System 2 cognitive processes, allows agents to
significantly enhance their performance by modifying their strategy. We tested
the metacognition module on a variety of scenarios, including a situation where
generative agents must survive a zombie apocalypse, and observe that our system
outperform others, while agents adapt and improve their strategies to complete
tasks over time.
Related papers
- Meta Reasoning for Large Language Models [58.87183757029041]
We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs)
MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task.
We evaluate the effectiveness of MRP through comprehensive benchmarks.
arXiv Detail & Related papers (2024-06-17T16:14:11Z) - Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm.
HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies.
HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
arXiv Detail & Related papers (2024-06-12T08:48:06Z) - Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments [0.0]
This thesis introduces an ontology-enhanced decision-making model (OntoDeM) for autonomous agents.
OntoDeM enriches agents' domain knowledge, allowing them to interpret unforeseen events, generate or adapt goals, and make better decisions.
Compared to traditional and advanced learning algorithms, OntoDeM shows superior performance in improving agents' observations and decision-making in dynamic, partially observable environments.
arXiv Detail & Related papers (2024-05-27T22:52:23Z) - STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making [43.734386326024016]
Large Language Models (LLMs) have revolutionized natural language processing, showing remarkable linguistic proficiency and reasoning capabilities.
This paper presents a novel framework equipped with memory and specialized tools to enhance their strategic decision-making capabilities.
arXiv Detail & Related papers (2024-05-25T23:25:10Z) - Rethinking ChatGPT's Success: Usability and Cognitive Behaviors Enabled by Auto-regressive LLMs' Prompting [5.344199202349884]
We analyze the structure of modalities within both two types of Large Language Models and six task-specific channels during deployment.
We examine the stimulation of diverse cognitive behaviors in LLMs through the adoption of free-form text and verbal contexts.
arXiv Detail & Related papers (2024-05-17T00:19:41Z) - A Survey on Self-Evolution of Large Language Models [116.54238664264928]
Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications.
To address this issue, self-evolution approaches that enable LLMs to autonomously acquire, refine, and learn from experiences generated by the model itself are rapidly growing.
arXiv Detail & Related papers (2024-04-22T17:43:23Z) - Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization [53.510942601223626]
Large Language Models (LLMs) exhibit robust problem-solving capabilities for diverse tasks.
These task solvers necessitate manually crafted prompts to inform task rules and regulate behaviors.
We propose Agent-Pro: an LLM-based Agent with Policy-level Reflection and Optimization.
arXiv Detail & Related papers (2024-02-27T15:09:20Z) - Empowering Large Language Model Agents through Action Learning [89.07382951897941]
Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error.
We argue that the capacity to learn new actions from experience is fundamental to the advancement of learning in LLM agents.
We introduce a framework LearnAct with an iterative learning strategy to create and improve actions in the form of Python functions.
arXiv Detail & Related papers (2024-02-24T13:13:04Z) - HAZARD Challenge: Embodied Decision Making in Dynamically Changing
Environments [93.94020724735199]
HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind.
This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
arXiv Detail & Related papers (2024-01-23T18:59:43Z) - Generalising via Meta-Examples for Continual Learning in the Wild [24.09600678738403]
We develop a novel strategy to deal with neural networks that "learn in the wild"
We equip it with MEML - Meta-Example Meta-Learning - a new module that simultaneously alleviates catastrophic forgetting.
We extend it by adopting a technique that creates various augmented tasks and optimises over the hardest.
arXiv Detail & Related papers (2021-01-28T15:51:54Z)
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.