Difference between revisions of "Game Theory"
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| − | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
| − | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | + | |
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| + | [https://www.youtube.com/results?search_query=game+Theory+artificial+intelligence+machine+learning+ML Youtube search...] | ||
| + | [https://www.google.com/search?q=game+Theory+artificial+intelligence+machine+learning+ML ...Google search] | ||
| − | [ | + | * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]] |
| − | [ | + | * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]] |
| − | + | * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]] | |
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| − | * [[ | ||
* [[Deep Distributed Q Network Partial Observability]] | * [[Deep Distributed Q Network Partial Observability]] | ||
* [[Markov Decision Process (MDP)]] | * [[Markov Decision Process (MDP)]] | ||
| + | * [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]] | ||
| + | * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]] | ||
| + | * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]] | ||
| − | Game Theory is a branch of mathematics used to model the strategic interaction between different players in a context with predefined rules and outcomes. Game Theory can be applied in different ambit of Artificial Intelligence: | + | |
| − | * Multi-agent AI systems. | + | Game Theory is a branch of mathematics used to model the strategic interaction between different players in a [[context]] with predefined rules and outcomes. Game Theory can be applied in different ambit of Artificial Intelligence: |
| + | * Multi-[[Agents|agent]] AI systems. | ||
* Imitation and [[Reinforcement Learning (RL)]]. | * Imitation and [[Reinforcement Learning (RL)]]. | ||
* Adversary training in [[Generative Adversarial Network (GAN)]]s. | * Adversary training in [[Generative Adversarial Network (GAN)]]s. | ||
| − | Game Theory can also be used to describe many situations in our daily life and Machine Learning models. [ | + | Game Theory can also be used to describe many situations in our daily life and Machine Learning models. [https://towardsdatascience.com/game-theory-in-artificial-intelligence-57a7937e1b88 Game Theory in Artificial Intelligence | Pier Paolo Ippolito - Towards Data Science] |
| + | For example, a Classification algorithm such as [[Support Vector Machine (SVM)]] can be explained in terms of a two-player game in which one player is challenging the other to find the best hyper-plane giving him the most difficult points to classify. The game will then converge to a solution which will be a trade-off between the strategic abilities of the two players (eg. how well the fist player was challenging the second one to classify difficult data points and how good was the second player to identify the best decision boundary). | ||
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| + | <youtube>MHS-htjGgSY</youtube> | ||
| + | <youtube>bHoXCtHPa0k</youtube> | ||
<youtube>jwlteKFyiHU</youtube> | <youtube>jwlteKFyiHU</youtube> | ||
<youtube>4WGbCJQU6BU</youtube> | <youtube>4WGbCJQU6BU</youtube> | ||
| + | |||
| + | == Nash Equilibrium == | ||
| + | An example of Nash Equilibrium can be when the [[Support Vector Machine (SVM)]] classifier agrees on which hyper-plane to use classify our data. | ||
| + | |||
| + | Nash equilibrium is a concept within game theory where the optimal outcome of a game is where there is no incentive to deviate from their initial strategy. More specifically, the Nash equilibrium is a concept of game theory where the optimal outcome of a game is one where no player has an incentive to deviate from his chosen strategy after considering an opponent's choice. Overall, an individual can receive no incremental benefit from changing actions, assuming other players remain constant in their strategies. A game may have multiple Nash equilibria or none at all. [https://www.investopedia.com/terms/n/nash-equilibrium.asp Nash Equilibrium | James Chen - Investopedia] | ||
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| + | <img src="https://www.investopedia.com/thmb/t5u0NwXOmsY-TRgrTiuRnJwgeD0=/1214x0/filters:no_upscale():max_bytes(150000):strip_icc():format(webp)/NashEquilibrium2-cbc58a27a37a4aab9585c3fc87938509.png" width="300" height="300"> | ||
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| + | <youtube>7oASpaBdDMs</youtube> | ||
| + | <youtube>FCGburaWXyc</youtube> | ||
| + | |||
| + | == Prisoner’s Dilemma == | ||
| + | |||
| + | The prisoner's dilemma is a paradox in decision analysis in which two individuals acting in their own self-interests do not produce the optimal outcome. The typical prisoner's dilemma is set up in such a way that both parties choose to protect themselves at the expense of the other participant. As a result, both participants find themselves in a worse state than if they had cooperated with each other in the decision-making process. The prisoner's dilemma is one of the most well-known concepts in modern game theory. [https://www.investopedia.com/terms/p/prisoners-dilemma.asp Prisoner's Dilemma | Jim Chappelow - Investopedia] | ||
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| + | https://miro.medium.com/max/568/1*N4LRMGzXdDSKUx--yeyfLQ.png | ||
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| + | <youtube>GsBWQMDhshI</youtube> | ||
| + | <youtube>6w7DrbaVwTc</youtube> | ||
Latest revision as of 22:04, 3 November 2024
Youtube search... ...Google search
- Gaming ... Game-Based Learning (GBL) ... Security ... Generative AI ... Games - Metaverse ... Quantum ... Game Theory ... Design
- Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
- Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Ernie | Baidu
- Deep Distributed Q Network Partial Observability
- Markov Decision Process (MDP)
- Python ... GenAI w/ Python ... JavaScript ... GenAI w/ JavaScript ... TensorFlow ... PyTorch
- Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
- Development ... Notebooks ... AI Pair Programming ... Codeless ... Hugging Face ... AIOps/MLOps ... AIaaS/MLaaS
Game Theory is a branch of mathematics used to model the strategic interaction between different players in a context with predefined rules and outcomes. Game Theory can be applied in different ambit of Artificial Intelligence:
- Multi-agent AI systems.
- Imitation and Reinforcement Learning (RL).
- Adversary training in Generative Adversarial Network (GAN)s.
Game Theory can also be used to describe many situations in our daily life and Machine Learning models. Game Theory in Artificial Intelligence | Pier Paolo Ippolito - Towards Data Science
For example, a Classification algorithm such as Support Vector Machine (SVM) can be explained in terms of a two-player game in which one player is challenging the other to find the best hyper-plane giving him the most difficult points to classify. The game will then converge to a solution which will be a trade-off between the strategic abilities of the two players (eg. how well the fist player was challenging the second one to classify difficult data points and how good was the second player to identify the best decision boundary).
Nash Equilibrium
An example of Nash Equilibrium can be when the Support Vector Machine (SVM) classifier agrees on which hyper-plane to use classify our data.
Nash equilibrium is a concept within game theory where the optimal outcome of a game is where there is no incentive to deviate from their initial strategy. More specifically, the Nash equilibrium is a concept of game theory where the optimal outcome of a game is one where no player has an incentive to deviate from his chosen strategy after considering an opponent's choice. Overall, an individual can receive no incremental benefit from changing actions, assuming other players remain constant in their strategies. A game may have multiple Nash equilibria or none at all. Nash Equilibrium | James Chen - Investopedia
:max_bytes(150000):strip_icc():format(webp)/NashEquilibrium2-cbc58a27a37a4aab9585c3fc87938509.png)
Prisoner’s Dilemma
The prisoner's dilemma is a paradox in decision analysis in which two individuals acting in their own self-interests do not produce the optimal outcome. The typical prisoner's dilemma is set up in such a way that both parties choose to protect themselves at the expense of the other participant. As a result, both participants find themselves in a worse state than if they had cooperated with each other in the decision-making process. The prisoner's dilemma is one of the most well-known concepts in modern game theory. Prisoner's Dilemma | Jim Chappelow - Investopedia
