Search Results for "action"
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Showing 11 open source projects for "action"
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AutoGPT
Powerful tool that lets you create and run intelligent agents
AutoGPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, AutoGPT pushes the boundaries of what is possible with AI. -
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LangGraph
Build resilient language agents as graphs
LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application,... -
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PyGOAPng
Python Goal Oriented Action Planning (GOAP) library
A library for implementing GOAP in an AI agent. Based on pygoap v3 by Leif Theden et al. Updated code to work without having pygame installed, bug-fixed functions to make them implement the behaviors that were expected, and implemented desired behaviors so that the Pirate demo works properly for all known actions and goals. -
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This project proposes a virtual laboratory for social scientists interested in the modelling, analysis and behavioral simulation of power relationships within organizations and more generally in systems of collective action. This software is grounded on the works lead by Sibertin-Blanc and Roggero on the formalization of the Sociology of Organised Action [Crozier and Freidberg]. Visit the homepage of the project on soclabproject.wordpress.com for tutorials, examples and further theoretical position.
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dnrDALMAS
A general-level Prolog implementation of the DALMAS architecture.
DnrDALMAS is a Prolog module intended to be a general-level Prolog implementation of the abstract DALMAS (Deontic Action-Logic based Multi-Agent System) architecture. A DALMAS is regulated by a normative system based on an algebraic version of the theory of normative positions. For more information about dnrDALMAS, see the following technical report: Hjelmblom, M. (2008). Deontic action-logic multi-agent systems in Prolog. University of Gävle, Division of Computer Science; University of Gävle. http://urn.kb.se/resolve?... -
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jDALMAS
A Java front-end to the dnrDALMAS Prolog Module.
A Java front-end to the dnrDALMAS Prolog Module, as described in the following paper: Hjelmblom, M., & Odelstad, J. (2009). jDALMAS: A Java/Prolog Framework for Deontic Action-Logic Multi-Agent Systems. In A. Håkansson, N. Nguyen, R. Hartung, R. Howlett, & L. Jain (Eds.), Agent and Multi-Agent Systems: Technologies and Applications (Vol. 5559, pp. 110–119). Berlin / Heidelberg: Springer. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-3963 -
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TinyCog
Cognitive architecture with a focus on self-models
TinyCog is a cognitive architecture (perception, action, reasoning, planning, and language) with a focus on representing "self-models" (physical, capabilities, intentions, social, historical, "cognitive stream") in order to implement social reasoning and collaborative planning in a "hunter domain" test bed. We use Prolog, RDF Triples, Description Logic and a HTN-Planner as base technologies. -
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This is a Draft project for implementation of Automate Tools for Mid-Sized Compagny for deployment and prototyping Standards and Regulation @work ! This will also lead to computability and prediction of effort Thru resolution of Human action @Work...
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POSH dynamic plans are technique for implementing Behaviour Oriented Design action selection for the control of intelligent systems. This is a Jython implementation. See http://www.cs.bath.ac.uk/~jjb/web/pyposh.html for a fuller description.
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HoldemAI
Texas Holdem Poker AI
Full ring Texas Hold'em poker game built around an intelligent AI system. The AI uses players' betting actions to calculate a probability distribution of their hole cards and uses it to evaluate hand strength and the best possible action. Small random changes are made to mimic human behavior and make the AI less predictable. Future versions will include adaptive opponent modeling using neural networks to improve the AI's strength. The AI code can be easily adapted for input from screen scrapers. -
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ALPprolog is a Prolog implementation of an action programming language. With ALPprolog you can program strategies for autonomous agents in dynamic domains like e.g. the Wumpus world.
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