AAAI at Stanford 2019, A Study of Basis on AI-based Information Systems:The Case of Shogi AI System "Ponanza" presentation yoda_mizukoshi_honjo_2019_slideshare
This study examines the Japanese chess (shogi) AI system Ponanza and its interactions with professional shogi players to gain insights into the relationship between humans and AI.
Ponanza became a mystery even to its developers as it improved to the point of defeating professional players through machine learning. Professional players were also unable to understand some of Ponanza's moves that went against shogi theory.
By discussing Ponanza's "black magic" with players, developers hope to understand how humans and AI can generate knowledge through dialogue and help humans learn to investigate the "reasons" behind AI decisions.
This document discusses artificial intelligence and its applications. It begins by listing common applications of AI such as marketing, banking, finance, agriculture, and healthcare. It then discusses daily applications like Google Maps, ride-sharing, autopilot, spam filters, and personal assistants. The document also covers robots using AI for assembly, customer service, packaging, and open-source systems. It provides definitions and approaches for AI including thinking humanly through cognitive modeling and the Turing test, thinking rationally through logical approaches, and acting rationally through the rational agent approach.
Artificial intelligence AI is the intelligence exhibited by an artificial entity, generally assumed to be a computer. It has been involved with gaming since day one. It is progressively being widely used in the gaming industry. AI in games is commonly used for creating players opponents. It is the foundation of all video games. Games like Nim, checkers, or chess took advantage of smart algorithms to beat human players. AI based games are based on a finite set of actions or reactions whose sequence can be easily predicted by expert players. This paper provides an introduction on the applications of AI in different games. Matthew N. O. Sadiku | Sarhan M. Musa | Abayomi Ajayi-Majebi "Artificial Intelligence in Gaming" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38516.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/38516/artificial-intelligence-in-gaming/matthew-n-o-sadiku
This document provides an overview of artificial intelligence, including definitions, history, and types. It defines AI as systems that can interpret data, learn from it, and achieve goals through adaptation like humans. The concepts of AI emerged in the 1950s from scientists like Turing who explored the possibility of machine intelligence. Modern AI uses machine learning, deep learning, and neural networks to analyze data and make predictions. AI is being applied in many areas like transportation, banking, social media and more. The document also discusses philosophical questions around AI's impact on jobs and possibility of surpassing humans. It concludes we should focus on creating devices to perform specific tasks rather than aiming for a single system superior to humans in all ways.
The ability of intuition and self- learning in humans is responsible for developing their
intelligence, reasoning and socialising. All this human characteristics can enable the robots to
volve into humans. In this context i explain that robots with developing intelligence can solve the problems of various scientific phenomenon such as black-hole, time travels and even in robotics the problems in sensors and actuators which do not impart human level DOF and movement thus making them do everything we can do. Imagine a robot doing yoga, karate, even a ballet all by itself without the rusty old controls and commands. Researchers have come with all kinds of robots and best of all social robots for social interaction so we have come with all kinds of robots what’s next? Robot scientists and researchers! Why not? It is highly evident that robot can think in new dimensions to solve issues.
The document proposes a Turing test for computer game bots to test their ability to imitate human game players. The test was implemented as a bot design competition called the 2Kbot prize contest. The competition involved bots playing the first person shooter game Unreal Tournament 2004 against human judges without knowing which was the bot. The judges would evaluate which players seemed more humanlike based on in-game actions and strategies.
The document provides an introduction to artificial intelligence, defining it as the study and design of intelligent agents that perceive their environment and take actions, and discusses some of the techniques used in AI like search, use of knowledge, and abstraction. It also outlines several applications of AI techniques like machine learning, deep learning, neural networks, and expert systems across domains such as natural language processing, computer vision, robotics, games, and more.
The document discusses an artificial intelligence course, outlining 5 units that will cover topics like problem solving through search algorithms, propositional logic, knowledge representation, planning, and learning from uncertainty. The goals of the course are for students to understand concepts like state space representation, heuristic search techniques, and applying AI techniques to problems involving games, machine learning and more. The course will examine algorithms, knowledge representation methods, and applications of AI in areas such as games, theorem proving and machine learning.
This document discusses artificial intelligence and its applications. It begins by listing common applications of AI such as marketing, banking, finance, agriculture, and healthcare. It then discusses daily applications like Google Maps, ride-sharing, autopilot, spam filters, and personal assistants. The document also covers robots using AI for assembly, customer service, packaging, and open-source systems. It provides definitions and approaches for AI including thinking humanly through cognitive modeling and the Turing test, thinking rationally through logical approaches, and acting rationally through the rational agent approach.
Artificial intelligence AI is the intelligence exhibited by an artificial entity, generally assumed to be a computer. It has been involved with gaming since day one. It is progressively being widely used in the gaming industry. AI in games is commonly used for creating players opponents. It is the foundation of all video games. Games like Nim, checkers, or chess took advantage of smart algorithms to beat human players. AI based games are based on a finite set of actions or reactions whose sequence can be easily predicted by expert players. This paper provides an introduction on the applications of AI in different games. Matthew N. O. Sadiku | Sarhan M. Musa | Abayomi Ajayi-Majebi "Artificial Intelligence in Gaming" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38516.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/38516/artificial-intelligence-in-gaming/matthew-n-o-sadiku
This document provides an overview of artificial intelligence, including definitions, history, and types. It defines AI as systems that can interpret data, learn from it, and achieve goals through adaptation like humans. The concepts of AI emerged in the 1950s from scientists like Turing who explored the possibility of machine intelligence. Modern AI uses machine learning, deep learning, and neural networks to analyze data and make predictions. AI is being applied in many areas like transportation, banking, social media and more. The document also discusses philosophical questions around AI's impact on jobs and possibility of surpassing humans. It concludes we should focus on creating devices to perform specific tasks rather than aiming for a single system superior to humans in all ways.
The ability of intuition and self- learning in humans is responsible for developing their
intelligence, reasoning and socialising. All this human characteristics can enable the robots to
volve into humans. In this context i explain that robots with developing intelligence can solve the problems of various scientific phenomenon such as black-hole, time travels and even in robotics the problems in sensors and actuators which do not impart human level DOF and movement thus making them do everything we can do. Imagine a robot doing yoga, karate, even a ballet all by itself without the rusty old controls and commands. Researchers have come with all kinds of robots and best of all social robots for social interaction so we have come with all kinds of robots what’s next? Robot scientists and researchers! Why not? It is highly evident that robot can think in new dimensions to solve issues.
The document proposes a Turing test for computer game bots to test their ability to imitate human game players. The test was implemented as a bot design competition called the 2Kbot prize contest. The competition involved bots playing the first person shooter game Unreal Tournament 2004 against human judges without knowing which was the bot. The judges would evaluate which players seemed more humanlike based on in-game actions and strategies.
The document provides an introduction to artificial intelligence, defining it as the study and design of intelligent agents that perceive their environment and take actions, and discusses some of the techniques used in AI like search, use of knowledge, and abstraction. It also outlines several applications of AI techniques like machine learning, deep learning, neural networks, and expert systems across domains such as natural language processing, computer vision, robotics, games, and more.
The document discusses an artificial intelligence course, outlining 5 units that will cover topics like problem solving through search algorithms, propositional logic, knowledge representation, planning, and learning from uncertainty. The goals of the course are for students to understand concepts like state space representation, heuristic search techniques, and applying AI techniques to problems involving games, machine learning and more. The course will examine algorithms, knowledge representation methods, and applications of AI in areas such as games, theorem proving and machine learning.
This document provides an overview of artificial intelligence, including its definition, history, types (narrow AI and general AI), machine learning techniques (supervised, unsupervised, semi-supervised, and reinforced learning), an example of an AI robot named Sophia, current applications of AI in mobile phones, games, GPS, and robotics, the future potential of AI such as in self-driving cars and medical diagnosis, as well as both the advantages and disadvantages of AI technology.
The application of point of view (POV) in electronic games has been vastly applied and fast becoming a
favorite among electronic games (EG) players particularly in games of action genre like warfare games.
While allowing the users to experience the character first-hand, POV has its limitations for users. One
example of the problems is the difficulty to anticipate the direction an attack by the enemy from a POV
blindspot. Another problem is the difficulty to prepare a strategy. Some players become “somewhat dizzy”
and eventually give up the game. This paper elaborates on the development of a framework for interactive
montage on EG software interface using ArTerma tools and how the ArTerma tool works in the POV
interface. Therefore, to accomplish the development of ArTerma tools, (A1(DDI)2E3) model is used. In
(A1(DDI)2E3) model usage, ADDIE model is combined with other model concept such as “Diegesis-
Spatiality” concept Model, Frame concept model, and Mental concept model has been employed along
side an elaboration on case studies. The study hopes that the developed a new model as well as support
tool can help improve future EGs.
Shologuti has three major component: move generation, search and evaluation. Each component are pretty much necessary, though evaluation with its quiescence analysis is the main part which makes each program’s play unique. To make this game more striking, most reliable algorithms and its many supporting aids are used here. Main components of the game tree search and pruning are analyzed here and the performance refinements such as aspiration variation search, assists like transposition and history table are compared here.
This webinar on artificial intelligence covered an introduction to AI, including definitions of intelligence and approaches to AI such as strong AI, weak AI, and applied AI. It demonstrated a question answering system and discussed application domains like natural language processing, computer vision, and self-driving cars. Principles of ethical AI like transparency, ownership of data and models, and augmenting human expertise were also outlined. The webinar concluded with remarks on issues concerning the use of AI.
INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND MACHINE LEARNINGsowmyamPSGRKCW
Unit 1 of the document introduces artificial intelligence and machine learning. It discusses how AI solves real-world problems by simulating human intelligence and modeling problem-solving processes. It also covers machine learning models like supervised, unsupervised, and reinforcement learning. Additionally, it introduces popular Python libraries for artificial intelligence like NumPy, Pandas, scikit-learn, and TensorFlow. The role of Python in AI is also discussed along with Anaconda and how to install Python libraries.
The role of ai in social games eladhari2011 uppsalauniMirjam Eladhari
The keynote discusses:
1) The role of AI in social games and different views on this topic.
2) How social actions can be modeled through operational logics in game design.
3) Examples of AI-based social game prototypes that incorporate elements like semi-autonomous avatars.
3) An experimental social game prototype called the Pataphysic Institute that uses an AI architecture called the Mind Module to model character personalities and emotions.
International Journal of Engineering Inventions (IJEI) provides a multidisciplinary passage for researchers, managers, professionals, practitioners and students around the globe to publish high quality, peer-reviewed articles on all theoretical and empirical aspects of Engineering and Science.
This document summarizes Maha Bali's presentation on teaching critical AI literacies. In 3 sentences:
The presentation discusses the need for developing critical AI literacy to have public deliberation on the risks and benefits of AI. It explores using metaphors to analyze perspectives on AI and proposes a framework involving dimensions like anthropomorphization. The framework and online resources like the AI Pedagogy Project can help educators develop assignments to teach critical thinking about AI.
This document outlines the syllabus for an Advanced Artificial Intelligence course. The course objectives are to learn the differences between optimal and human-like reasoning, understand state space representation and complexity, learn methods for solving problems using AI, be introduced to machine learning concepts, and learn probabilistic reasoning techniques. The syllabus covers topics like search strategies, constraint satisfaction problems, games, knowledge representation, planning, and uncertainty. Recommended textbooks are also listed.
A.I Introduction (Introduction, Producion Rues, Intelligence, Knowledge)Tanishq Soni
This document provides an overview of artificial intelligence including:
- Definitions of AI as the ability of computers to mimic human intelligence and perform human-like tasks.
- The key components of intelligence like learning, reasoning, perception, problem solving, and language.
- The four main types of AI systems: computer systems, expert systems, intelligent systems, and four forms of AI based on capabilities.
- An introduction to important AI concepts like knowledge, production rules, forward and backward chaining, and the steps to construct a production rules system.
The Effectiveness of using a Historical Sequence-based Predictor Algorithm in...AM Publications,India
Games can be used as simulations to check the effectiveness and viability of an Artificial Intelligence technique. In this study, a competitive RoShamBo AI is developed and its performance is measured using the first International RoShamBo Tournament test suite. The AI stores a history of its opponent's move and checks for repetition to predict the opponent's next play. A training program was also developed that finds the best performing bot variant by changing the bot's behavior in terms of increasing the history's window size. The developed AI is shown to rank high in the competition. This indicates the potential of using the core technique (of the proposed variant) as an Artificial Intelligence bot to similarly applicable games.
Research Overview Mirjam P Eladhari August 2019Mirjam Eladhari
Slides for a presentation where I gave an overview of my research in August 2019. The talk is about how I have adressed two question that are at the core of my work:
Q1 How can we work to innovate in game design and technology?
Q2 How can we create play experiences that are individually meaningful?
Ian Bogost’s concept of procedural rhetoric is a tantalising theory of the power and potential of computer games, especially serious games. Yet does this concept really distinguish games from other media? Can this concept be usefully applied to the design and critique of serious games? This paper explores the ramifications of games (particularly serious games) as procedural rhetoric and whether this concept is problematic, useful, inclusive, or better employed as a recalibrated meta-epistemic theory of serious games that persuade or suggest to the player that the game mechanics, game genre, or digitally simulated world-view is open to criticism and reflection.
Artificial intelligence is becoming increasingly prevalent in daily life. While AI has many applications and benefits, it also poses some challenges and concerns. AI is used for tasks like playing games, medical diagnoses, managing social media data, and self-driving cars. However, advanced chatbots like ChatGPT have caused disturbances by enabling cheating and writing papers for students. As AI systems continue to evolve and learn on their own from massive online data, some experts worry that AI may eventually surpass human levels of intelligence and potentially pose risks if not developed safely. Overall, AI research aims to address challenges around employment impacts and ensuring AI systems remain beneficial to humanity.
This document provides an introduction to the topic of artificial intelligence (AI). It defines AI as the study of intelligent systems, including systems that learn, reason, understand language, and perceive visual scenes like humans. The major branches of AI are described, as are the foundations in fields like philosophy, mathematics, neuroscience, and computer science. The history of AI from its origins to modern applications is outlined. Philosophical debates regarding whether machines can truly be intelligent are discussed. Finally, an introduction to logic programming languages like Prolog is provided.
Interactive System for Collaborative Historical AnalogyRyo YOSHIKAWA
1) The authors propose an interactive system to support collaborative historical analogy in education.
2) The system uses clustering algorithms to group students with differing perspectives to enhance discussion. It provides a collaborative text editor and chat for group work.
3) An evaluation found the system took less than a second on average to make groups. A teacher interview found it improved collaborative editing and application of historical events.
The document discusses the history and development of machine learning, with a focus on Arthur Samuel who is considered the creator of the field. It also provides an overview of machine learning, the different types, and compares the most popular programming languages for machine learning applications - Python and R. The document argues that Python is generally more preferable than R for machine learning due to its large and diverse library ecosystem and seamless integration of Python with other programming languages.
Applications of Artificial Intelligence & Associated Technologiesdbpublications
This paper reviews the meaning of artificial intelligence and its various advantages and disadvantages including its applications. It also considers the current progress of this technology in the real world and discusses the applications of AI in the fields of heavy industries, gaming, aviation, weather forecasting, expert systems with the focus being on expert systems. The paper concludes by analyzing the future potential of Artificial Intelligence.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
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Similar to AAAI at Stanford 2019, A Study of Basis on AI-based Information Systems:The Case of Shogi AI System "Ponanza" presentation yoda_mizukoshi_honjo_2019_slideshare
This document provides an overview of artificial intelligence, including its definition, history, types (narrow AI and general AI), machine learning techniques (supervised, unsupervised, semi-supervised, and reinforced learning), an example of an AI robot named Sophia, current applications of AI in mobile phones, games, GPS, and robotics, the future potential of AI such as in self-driving cars and medical diagnosis, as well as both the advantages and disadvantages of AI technology.
The application of point of view (POV) in electronic games has been vastly applied and fast becoming a
favorite among electronic games (EG) players particularly in games of action genre like warfare games.
While allowing the users to experience the character first-hand, POV has its limitations for users. One
example of the problems is the difficulty to anticipate the direction an attack by the enemy from a POV
blindspot. Another problem is the difficulty to prepare a strategy. Some players become “somewhat dizzy”
and eventually give up the game. This paper elaborates on the development of a framework for interactive
montage on EG software interface using ArTerma tools and how the ArTerma tool works in the POV
interface. Therefore, to accomplish the development of ArTerma tools, (A1(DDI)2E3) model is used. In
(A1(DDI)2E3) model usage, ADDIE model is combined with other model concept such as “Diegesis-
Spatiality” concept Model, Frame concept model, and Mental concept model has been employed along
side an elaboration on case studies. The study hopes that the developed a new model as well as support
tool can help improve future EGs.
Shologuti has three major component: move generation, search and evaluation. Each component are pretty much necessary, though evaluation with its quiescence analysis is the main part which makes each program’s play unique. To make this game more striking, most reliable algorithms and its many supporting aids are used here. Main components of the game tree search and pruning are analyzed here and the performance refinements such as aspiration variation search, assists like transposition and history table are compared here.
This webinar on artificial intelligence covered an introduction to AI, including definitions of intelligence and approaches to AI such as strong AI, weak AI, and applied AI. It demonstrated a question answering system and discussed application domains like natural language processing, computer vision, and self-driving cars. Principles of ethical AI like transparency, ownership of data and models, and augmenting human expertise were also outlined. The webinar concluded with remarks on issues concerning the use of AI.
INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND MACHINE LEARNINGsowmyamPSGRKCW
Unit 1 of the document introduces artificial intelligence and machine learning. It discusses how AI solves real-world problems by simulating human intelligence and modeling problem-solving processes. It also covers machine learning models like supervised, unsupervised, and reinforcement learning. Additionally, it introduces popular Python libraries for artificial intelligence like NumPy, Pandas, scikit-learn, and TensorFlow. The role of Python in AI is also discussed along with Anaconda and how to install Python libraries.
The role of ai in social games eladhari2011 uppsalauniMirjam Eladhari
The keynote discusses:
1) The role of AI in social games and different views on this topic.
2) How social actions can be modeled through operational logics in game design.
3) Examples of AI-based social game prototypes that incorporate elements like semi-autonomous avatars.
3) An experimental social game prototype called the Pataphysic Institute that uses an AI architecture called the Mind Module to model character personalities and emotions.
International Journal of Engineering Inventions (IJEI) provides a multidisciplinary passage for researchers, managers, professionals, practitioners and students around the globe to publish high quality, peer-reviewed articles on all theoretical and empirical aspects of Engineering and Science.
This document summarizes Maha Bali's presentation on teaching critical AI literacies. In 3 sentences:
The presentation discusses the need for developing critical AI literacy to have public deliberation on the risks and benefits of AI. It explores using metaphors to analyze perspectives on AI and proposes a framework involving dimensions like anthropomorphization. The framework and online resources like the AI Pedagogy Project can help educators develop assignments to teach critical thinking about AI.
This document outlines the syllabus for an Advanced Artificial Intelligence course. The course objectives are to learn the differences between optimal and human-like reasoning, understand state space representation and complexity, learn methods for solving problems using AI, be introduced to machine learning concepts, and learn probabilistic reasoning techniques. The syllabus covers topics like search strategies, constraint satisfaction problems, games, knowledge representation, planning, and uncertainty. Recommended textbooks are also listed.
A.I Introduction (Introduction, Producion Rues, Intelligence, Knowledge)Tanishq Soni
This document provides an overview of artificial intelligence including:
- Definitions of AI as the ability of computers to mimic human intelligence and perform human-like tasks.
- The key components of intelligence like learning, reasoning, perception, problem solving, and language.
- The four main types of AI systems: computer systems, expert systems, intelligent systems, and four forms of AI based on capabilities.
- An introduction to important AI concepts like knowledge, production rules, forward and backward chaining, and the steps to construct a production rules system.
The Effectiveness of using a Historical Sequence-based Predictor Algorithm in...AM Publications,India
Games can be used as simulations to check the effectiveness and viability of an Artificial Intelligence technique. In this study, a competitive RoShamBo AI is developed and its performance is measured using the first International RoShamBo Tournament test suite. The AI stores a history of its opponent's move and checks for repetition to predict the opponent's next play. A training program was also developed that finds the best performing bot variant by changing the bot's behavior in terms of increasing the history's window size. The developed AI is shown to rank high in the competition. This indicates the potential of using the core technique (of the proposed variant) as an Artificial Intelligence bot to similarly applicable games.
Research Overview Mirjam P Eladhari August 2019Mirjam Eladhari
Slides for a presentation where I gave an overview of my research in August 2019. The talk is about how I have adressed two question that are at the core of my work:
Q1 How can we work to innovate in game design and technology?
Q2 How can we create play experiences that are individually meaningful?
Ian Bogost’s concept of procedural rhetoric is a tantalising theory of the power and potential of computer games, especially serious games. Yet does this concept really distinguish games from other media? Can this concept be usefully applied to the design and critique of serious games? This paper explores the ramifications of games (particularly serious games) as procedural rhetoric and whether this concept is problematic, useful, inclusive, or better employed as a recalibrated meta-epistemic theory of serious games that persuade or suggest to the player that the game mechanics, game genre, or digitally simulated world-view is open to criticism and reflection.
Artificial intelligence is becoming increasingly prevalent in daily life. While AI has many applications and benefits, it also poses some challenges and concerns. AI is used for tasks like playing games, medical diagnoses, managing social media data, and self-driving cars. However, advanced chatbots like ChatGPT have caused disturbances by enabling cheating and writing papers for students. As AI systems continue to evolve and learn on their own from massive online data, some experts worry that AI may eventually surpass human levels of intelligence and potentially pose risks if not developed safely. Overall, AI research aims to address challenges around employment impacts and ensuring AI systems remain beneficial to humanity.
This document provides an introduction to the topic of artificial intelligence (AI). It defines AI as the study of intelligent systems, including systems that learn, reason, understand language, and perceive visual scenes like humans. The major branches of AI are described, as are the foundations in fields like philosophy, mathematics, neuroscience, and computer science. The history of AI from its origins to modern applications is outlined. Philosophical debates regarding whether machines can truly be intelligent are discussed. Finally, an introduction to logic programming languages like Prolog is provided.
Interactive System for Collaborative Historical AnalogyRyo YOSHIKAWA
1) The authors propose an interactive system to support collaborative historical analogy in education.
2) The system uses clustering algorithms to group students with differing perspectives to enhance discussion. It provides a collaborative text editor and chat for group work.
3) An evaluation found the system took less than a second on average to make groups. A teacher interview found it improved collaborative editing and application of historical events.
The document discusses the history and development of machine learning, with a focus on Arthur Samuel who is considered the creator of the field. It also provides an overview of machine learning, the different types, and compares the most popular programming languages for machine learning applications - Python and R. The document argues that Python is generally more preferable than R for machine learning due to its large and diverse library ecosystem and seamless integration of Python with other programming languages.
Applications of Artificial Intelligence & Associated Technologiesdbpublications
This paper reviews the meaning of artificial intelligence and its various advantages and disadvantages including its applications. It also considers the current progress of this technology in the real world and discusses the applications of AI in the fields of heavy industries, gaming, aviation, weather forecasting, expert systems with the focus being on expert systems. The paper concludes by analyzing the future potential of Artificial Intelligence.
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AAAI at Stanford 2019, A Study of Basis on AI-based Information Systems:The Case of Shogi AI System "Ponanza" presentation yoda_mizukoshi_honjo_2019_slideshare
1. A Study of Basis on AI-based Information Systems:
The Case of Shogi AI System “Ponanza”
Yuichi Yoda, Ph.D. in Business Administration
Visiting Scholar at US Asia Technology Management Center, Stanford Univ.
Associate Professor at Ritsumeikan University, Japan
Kosuke Mizukoshi, Ph.D. in Commercial Science
Associate Professor at Tokyo Metropolitan University, Japan
Seiichiro Honjo, Ph.D.
Associate Professor at Shizuoka University, Japan
1
March 25th, 2019
Association for Advancement of Artificial Intelligence
at Stanford University
2. Background of Research Interest
– On the recommendation system of Amazon.com and
the advertising system of Google, even business
people who are in charge of these systems are
seeking out the basis on why it works.
(Yoda Y, Mizukoshi K. and Honjo S. , 2016)
– Marketing and Business Administration research has
not been aligned with an AI system.
– Why?
2
What is the impact of AI to the research of marketing
and business administration in social science?
3. Objectives
– To deepen our understanding about the
exploration of AI in corporate marketing
– To interpret how people and society respond in
their attempt to comprehend the development
and actions of AI.
3
This study focuses on the relationship between
AI and humans in terms of basis
4. Keywords
“A Study of Basis on AI-based Information Systems:
The Case of Shogi AI System “Ponanza” ”
4
Keywords:
・basis
・cause
・reason
・logical reasoning
・ machine learning
5. Summary
– Shogi AI “Ponanza” became a mystery even for
its developers in their process of building this
system into one capable of defeating
professional Shogi players
– It is now open to interpretation for its
developers and professionals.
5
In this study, we discuss the case of Ponanza, an AI
based system for Japanese Chess “Shogi” and
professional Shogi players because what happens
between Shogi AI and professional Shogi players can
partially help humans and AI get clues in marketing.
6. Summary: Conclusion
– Specifically, when we treat AI as an extension of
humans, it will be important to consider how AI
and humans create knowledge and how humans
can learn from AI.
– In the future, interaction with AI can be expected
to improve human’s ability to investigate
“causes” and develop “reasons”.
6
How do AI and humans create knowledge?
8. Method: Case study
• Use of multiple sources
• Mutual confirmation of primary and
secondary data
• Use of face-to-face interview
• Maintenance of a chain of evidence 8
Scientific research method using qualitative
data (Yin, 1994)
9. Method: Case study
• Shogi is a game with fixed rules, played in a static
environment and can therefore be studied as a case
separated from the complicated and dynamic
environment of society as business
• Professional Shogi players are considered one of the
representative examples of the human intellect
• Superiority dispute between AI and humans is already
settled. Shogi AI far surpassed Shogi players.
9
Why did we focus on Shogi AI at first?
What happens between Shogi AI and professional players
can partially help humans and AI get clues in marketing
11. Overview of Ponanza
– As Shogi AI, it defeated a professional Shogi
player for the first time on March 30, 2013.
– On May 20, 2017, it became the first Shogi AI to
beat an active Shogi “Meijin” which is the most
prestigious title of Shogi in Japan.
11
Ponanza is a Shogi program that Issei Yamamoto
developed
12. History
Table 1 Major matches between Shogi AI and Shogi players
Year Details
2007
Exhibition match between Bonanza (AI) and Akira Watanabe,
Ryuou (Winner)
Bonanza (AI) made open source
*partially used as reference for Ponanza too
2012 Bonkras (AI Winner) vs. Kunio Yonenaga, Eisei Kisei
2013
Ponanza (AI Winner) vs. Shinichi Satoh, 4-dan(
*Shogi AI’s first victory over an active professional Shogi player
2014 Ponanza (AI Winner) vs. Nobuyuki Yashiki, 9-dan
2015
Ponanza (AI Winner) vs. Yasuaki Murayama, 9-dan
Winner of the 25th World Computer Shogi Championship
2016 Ponanza (AI Winner) vs. Takayuki Yamasaki, 8-dan
2017
Ponanza (Ai Winner) vs. Amahiko Satoh, Meijin (Highest title)
Shogi AI’s first victory over an active Meijin
12
13. (1) Overview of Ponanza
– Exploration, refers to the ability to predict and
correctly emulate the future (make a guess
without adding one’s subjective views or
judgement) called reading.
– However, because it is difficult to completely
explore all of the large number of situations due to
resource constraints, computers determine the
next move by prioritizing choices. This process
of marking highlights is referred to as evaluation.
13
Shogi AI requires 2 functions as in the case of
human intellectual activities
14. (1) Overview of Ponanza
– Exploration The area of exploration is gradually
reduced, as needed, to effectively use the limited
resources.
– Evaluation Humans program the “exploration”
part, which was a main function, and specified how
the exploration was to be conducted, while the
computer learns to “evaluate” by itself through the
introduction of machine learning 14
Shogi AI requires 2 functions as in the case of
human intellectual activities
15. (1) Overview of Ponanza
– Ponanza needed a function to express the
adjustments between the more than one hundred
million parameters as “evaluation parameters”
in order to represent the complexity of Shogi,
based on three-piece relationships, including the
King.
– Ponanza could play moves similar to 45% of the
Shogi players through 50,000 games as training
data.
15
The 1st Version of Ponanza
16. (1) Overview of Ponanza
– Machine learning based parameter adjustments
by computers are faster and more accurate than
manual parameter adjustments by humans.
– Therefore, Yamamoto decided to thoroughly train
(adjust parameters) the computer for the
parameter function and devoted himself to
describing through a program how the computer
should be trained to evaluate.
16
On March 30, 2013 for the first time, AI defeated
an active Shogi player
17. (1) Overview of Ponanza
– In 2014, Yamamoto introduced reinforcement
learning, which is unsupervised learning, after
working on supervised learning where Ponanza
learned from game records of Shogi players.
– He accumulated about eight billion such
situations and has eventually analyzed nearly one
trillion situations. This process results in
determining new Ponanza-style tactics, which
refers to sequences that do not exist in games
played between humans. 17
Shogi AI’s first victory over an active Meijin in 2017
18. (2) Developer’s Perspective
– As Ponanza’s performance improves, it is becoming
more and more difficult to be explained.
Yamamoto compared its mystery to “Black magic”
– Yamamoto says that he “had no clue” about the
workings of the improvements that proved
effective. In concrete terms, he says that he does
not understand the real reason why the values
fed in the program work or why a certain
combination of values is effective.
18
Black magic?
19. (2) Developer’s Perspective
– For example, Idle parallelization: Multiple cores of
the CPU separately carry out the same processing
and the effective methods that each core
accidently discovers are shared with the entire
system.
– Interestingly, even experts find it difficult to explain
why randomly shared methods work well.
– Best possible explanation is that “an experiment
turned out well.”
19
Black magic?
20. (3) Shogi Player’s Perspective
– For example, Shogi AI made an exceptional move
to build defense by giving up a piece to the Meijin
who had no attacking pieces. This was against
Shogi theory,
– The opponent, Satoh Meijin, he was unable to
understand the meaning of the move because
he held preconceived notions such as sacrificing
a pawn when the opponent has two pieces that are
effective. He said that he was unable to anticipate
the move.
20
Shogi AI Intelligence?
21. (3) Shogi Player’s Perspective
– Such as sacrificing a pawn when the opponent
has two pieces that are effective. (Satoh Meijin,
NHK, July 31, 2017).
– Moreover, Yoshiharu Habu explained “humans
found it difficult to imagine a situation where a
player would use a piece that is neither
attacking nor defending, and even give up a
pawn to the opponent who does not have one”.
(Habu, NHK 2017). 21
Shogi players could express the reasons by
their contextual expressions by language after
deep consideration
22. (3) Shogi Player’s Perspective
– Satoh Meijin says that this showed that there could
be best moves in Shogi that humans do not see
any reason for (that humans find difficult to
understand). (Satoh Meijin, NHK 2017).
– Shogi players are beginning to find ways to learn
from Shogi AI
– For example. Shogi players are placing importance
on learning positioning judgment from Shogi AI.
22
“Shogi players have been learning from AI
through dialogue”
24. Discussion
24
3 Stages of Collaboration by Humans and AI
Stage
Strength /
Knowledge
How to Actualize
1st
“Imitation”
Humans
Supervised Learning by
Humans Data
2nd
“Implemen-
tation”
AI
Humans Attempted to Adjust
Parameters with
Trial and Error
3rd
“Black
Magic”
Internalized
in AI
Humans use Language
to Understand and Explain
Why
We could create knowledge through interaction
25. Discussion
25
Humans are capable of taking two kinds of “Basis”
approaches. Cause and Reason in philosophy
How to treat
AI
Characteristics
Basis
(Why, Foundation)
AI as a
Physical
Phenomenon
A certain result is produced under
certain conditions, even though we
do not understand the logic behind it.
An investigation of
the “cause” of a
phenomena without
depending on
language.
As an
Extension to
Humans
Humans and society, not AI, ask the
foundation as to “why” AI is able to
produce certain results. That is why,
contextual and language-based
“reasons” should be expected by
humans and society.
Building a model and
seek a “reason” as a
basis for
understanding.
26. Discussion
– To produce a logic to justify the basis just like
logical reasoning rather than discovering a
working principle or “cause.”
– During externalization in the SECI model (Nonaka
and Takeuchi, 1995), tacit knowledge is converted
into explicit knowledge through dialogue
between individuals. The concept of dialogue
between two humans may be extended to
imagine an interaction between AI and humans
where the latter learns from the former. 26
What is the challenge for humans?
How do AI and humans create knowledge?
27. Conclusion
– When we treat AI as an extension of humans (not
a physical phenomenon), it will be important to
consider how AI and humans create knowledge and
how humans can learn from AI.
– Interaction with AI can be expected to improve
human’s ability to investigate “causes” and
develop “reasons” by logical reasoning.
– Dialogue between AI and humans can create
new knowledge.
27
Key Implications
28. Acknowledgments
28
In this study, we learned immensely from Issei Yamamoto,
the developer of Shogi AI Ponanza, about his experiences and
knowledge through the interview with him. We also received a
wealth of information about the perspectives of Shogi players
from Seiya Tomita, a member of the Japan Shogi Association.
We are grateful to both for their invaluable support.
Funding from the Telecommunication Advanced Foundation,
Ritsumeikan University, Japan Marketing Academy, and JSPS
KAKENHI Grant Number JP18K12878 is gratefully acknowledged.
29. References
29
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Shogi AI:Diamond [Kindle version] Retrieved from Amazon.com
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