Modular Design Patterns for Hybrid Actors
- URL: http://arxiv.org/abs/2109.09331v1
- Date: Mon, 20 Sep 2021 07:19:54 GMT
- Title: Modular Design Patterns for Hybrid Actors
- Abstract summary: This paper is an extension of the boxology to describe distributed hybrid AI systems with actors and interactions.
We show examples using a few design patterns relevant in multi-agent systems and human-agent interaction in general and, specifically, in the manufacturing domain.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, a boxology (graphical language) with design patterns for hybrid AI
was proposed, combining symbolic and sub-symbolic learning and reasoning. In
this paper, we extend this boxology with actors and their interactions. The
main contributions of this paper are: 1) an extension of the taxonomy to
describe distributed hybrid AI systems with actors and interactions; and 2)
showing examples using a few design patterns relevant in multi-agent systems
and human-agent interaction in general and, specifically, in the manufacturing
domain.
Related papers
- Social World Model-Augmented Mechanism Design Policy Learning [58.739456918502704]
We introduce SWM-AP (Social World Model-Augmented Mechanism Design Policy Learning), which learns a social world model hierarchically to enhance mechanism design.<n>We show that SWM-AP outperforms established model-based and model-free RL baselines in cumulative rewards and sample efficiency.
arXiv Detail & Related papers (2025-10-22T06:01:21Z) - Large Language Model Agent: A Survey on Methodology, Applications and Challenges [88.3032929492409]
Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence.
This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy.
Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time.
arXiv Detail & Related papers (2025-03-27T12:50:17Z) - AI Automatons: AI Systems Intended to Imitate Humans [54.19152688545896]
There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness.
The research, design, deployment, and availability of such AI systems have prompted growing concerns about a wide range of possible legal, ethical, and other social impacts.
arXiv Detail & Related papers (2025-03-04T03:55:38Z) - Survey of User Interface Design and Interaction Techniques in Generative AI Applications [79.55963742878684]
We aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike.
We also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
arXiv Detail & Related papers (2024-10-28T23:10:06Z) - A process algebraic framework for multi-agent dynamic epistemic systems [55.2480439325792]
We propose a unifying framework for modeling and analyzing multi-agent, knowledge-based, dynamic systems.
On the modeling side, we propose a process algebraic, agent-oriented specification language that makes such a framework easy to use for practical purposes.
arXiv Detail & Related papers (2024-07-24T08:35:50Z) - Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams [2.237039275844699]
Generative AI models are increasingly being integrated into human task, enabling the production of expressive content.
Unlike traditional human-AI design methods, the new approach to designing generative capabilities focuses heavily on prompt engineering strategies.
Our findings highlight emerging practices and role shifts in AI system prototyping among multistakeholder teams.
arXiv Detail & Related papers (2024-02-27T17:56:10Z) - Unpacking Human-AI interactions: From interaction primitives to a design
space [6.778055454461106]
We show how these primitives can be combined into a set of interaction patterns.
The motivation behind this is to provide a compact generalisation of existing practices.
We discuss how this approach can be used towards a design space for Human-AI interactions.
arXiv Detail & Related papers (2024-01-10T12:27:18Z) - Graph-Convolutional Autoencoder Ensembles for the Humanities,
Illustrated with a Study of the American Slave Trade [0.0]
We introduce a graph-aware autoencoder ensemble framework, with associated formalisms and tooling.
By composing sub-architectures to produce a model we maintain interpretability while providing function signatures for each sub-architectural choice.
We illustrate a practical application of our approach to a historical study of the American post-Atlantic slave trade.
arXiv Detail & Related papers (2024-01-01T17:48:25Z) - Hybrid Modeling Design Patterns [10.266928164137635]
We provide four base patterns that can serve as blueprints for combining data-driven components with domain knowledge into a hybrid approach.
We also present two composition patterns that govern the combination of the base patterns into more complex hybrid models.
Each design pattern is illustrated by typical use cases from application areas such as climate modeling, engineering, and physics.
arXiv Detail & Related papers (2023-12-29T15:40:38Z) - On the Discussion of Large Language Models: Symmetry of Agents and
Interplay with Prompts [51.3324922038486]
This paper reports the empirical results of the interplay of prompts and discussion mechanisms.
It also proposes a scalable discussion mechanism based on conquer and merge.
arXiv Detail & Related papers (2023-11-13T04:56:48Z) - Discrete, compositional, and symbolic representations through attractor dynamics [51.20712945239422]
We introduce a novel neural systems model that integrates attractor dynamics with symbolic representations to model cognitive processes akin to the probabilistic language of thought (PLoT)
Our model segments the continuous representational space into discrete basins, with attractor states corresponding to symbolic sequences, that reflect the semanticity and compositionality characteristic of symbolic systems through unsupervised learning, rather than relying on pre-defined primitives.
This approach establishes a unified framework that integrates both symbolic and sub-symbolic processing through neural dynamics, a neuroplausible substrate with proven expressivity in AI, offering a more comprehensive model that mirrors the complex duality of cognitive operations
arXiv Detail & Related papers (2023-10-03T05:40:56Z) - Neurosymbolic AI and its Taxonomy: a survey [48.7576911714538]
Neurosymbolic AI deals with models that combine symbolic processing, like classic AI, and neural networks.
This survey investigates research papers in this area during recent years and brings classification and comparison between the presented models as well as applications.
arXiv Detail & Related papers (2023-05-12T19:51:13Z) - Knowledge-Aware Bayesian Deep Topic Model [50.58975785318575]
We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling.
Our proposed model efficiently integrates the prior knowledge and improves both hierarchical topic discovery and document representation.
arXiv Detail & Related papers (2022-09-20T09:16:05Z) - Mimetic Models: Ethical Implications of AI that Acts Like You [5.843033621853535]
An emerging theme in artificial intelligence research is the creation of models to simulate the decisions and behavior of specific people.
We develop a framework for characterizing the ethical and social issues raised by their growing availability.
arXiv Detail & Related papers (2022-07-19T16:41:36Z) - Modular Design Patterns for Hybrid Learning and Reasoning Systems: a
taxonomy, patterns and use cases [0.3849857432787594]
unification of statistical (data-driven) and symbolic (knowledge-driven) methods is one of the key challenges of modern AI.
Recent years have seen large number of publications on such hybrid neuro-symbolic AI systems.
We propose a set of modular design patterns for such hybrid, neuro-symbolic systems.
arXiv Detail & Related papers (2021-02-23T22:16:05Z)
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.