Negotiation
YouTube search... ...Google search
- Assistants ..Hybrid Assistants
- Agents
- ChatGPT | OpenAI
- Attention Mechanism/Transformer Model
- Reinforcement Learning (RL) from Human Feedback (RLHF)
- Proximal Policy Optimization (PPO)
- Natural Language Generation (NLG)
- Natural Language Tools & Services
"Hybrid assistants" or "hybrid chatbots" architecture links the assistant to agent(s) when the assistant is unable to answer a user's query, the conversation can be transferred to an software agent or live person to obtain the required information. The goal of this integration is to provide a seamless and efficient user experience, where the assistant can handle simple inquiries and the agent(s) can handle more complex issues to improve user satisfaction. The assistant can interface with the software agent by sending and receiving data between the two systems. The assistant acts as the interface for the user, receiving inputs and providing outputs, while the software agent performs actions based on the information received from the assistant.
Here's an example of how a assistant might interface with a software agent:
- The user interacts with the assistant, providing information about a task they want to accomplish.
- The assistant processes the user's request and sends the relevant information to the software agent.
- The software agent performs the necessary actions based on the information received from the assistant, such as accessing a database, making calculations, or automating a process.
- The software agent sends the results back to the assistant, which then presents the information to the user in a conversational format.
This type of interface allows the assistant to perform complex tasks and access information that might not be immediately available to the assistant alone, while still providing the user with an easy-to-use conversational interface.
Negotiation
- ChatGPT | OpenAI
- Gaming
- How artificial intelligence could negotiate better deals for humans | Matthew Hutson - Science
- Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy | J. Kramár, T. Eccles, I. Gemp, A. Tacchetti, K. McKee, M. Malinowski, T. Graepel & Y. Bachrach - Nature Communications
- Companies Are Adopting AI For Supplier Negotiations, But Which Ones Should The Machines Handle? | Martin Rand - Forbes
- The Contract Negotiation Process Streamlined by AI - Gary Sangha - LexCheck
- AI enabled Negotiations: Are we on board yet? | Kiran Huthanahalli Manjunath - Infosys BPM
Meta
- Cicero from meta may foreshadow hybrid AI future architectures | Ajit Jaokar - Data Science Central
- DeepMind vs CICERO: How Meta Defeated Alphabet at its Own Game | Anirudh Vk - AIM
“An agent that can play at the level of humans in a game as strategically complex as Diplomacy is a true breakthrough for cooperative AI.” - Yann LeCun
Cicero couples a dialogue module with a strategic reasoning engine. Each turn, CICERO models the other players’ policies based on the game state and shared dialogue. It forms a plan, and the dialogue module generates messages conditional on the plan. Diplomacy is a game whose main element is negotiation. For this reason, in order for AI to play Diplomacy and win, ``talk with other players without discomfort and call for cooperation, ``interpret strategies from other players' negotiations, ``see other players' lies and It is necessary to perform difficult processing for existing AI, such as 'laying a lie to the player of'. In order to solve these problems, Meta uses both 'strategic reasoning' used in the strongest Go AI 'AlphaGo' and 'natural language processing' used in sentence generation AI 'GPT-3'. Meta develops AI ` Cicero ' that manipulates ` lies ' to deceive humans and bites into the top 10% of the friendship destruction game ` Diplomacy ' | Maxime Bonzi - Gigazine
Meta's new AI can beat human players at Diplomacy | Matt Cox - Rock Paper Shotgun ... It never "intentionally" lies
Google DeepMind
- AI for the board game Diplomacy | DeepMind
- GitHub Google Diplomacy
- Learning to Play No-Press Diplomacy with Best Response Policy Iteration | T. Anthony, T. Eccles, A. Tacchetti, J. Kramár, I. Gemp, T. Hudson, N. Porcel, M. Lanctot, J. Pérolat, R. Everett, R. Werpachowski, S. Singh, T. Graepel & Y. Bachrach - Deepmind
- Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy | J. Kramár, T. Eccles, I. Gemp, A. Tacchetti, K. McKee, M. Malinowski, T. Graepel & Y. Bachrach - Nature Communications DeepMind