GameLabWiki - User contributions [en]

Computer Game AI

2021-01-18T11:52:05Z

Lena.kamenzin:

Computer Game AI is the use of Artificial Intelligence in computer games in a broader sense, that is, "to create a compelling experience for the player."<ref name=":0" /> The first Computer Game AI ever to beat a human player in a specific game is the chess computer [[deep blue]].

==Introduction==
Artificial Intelligence (AI) is used in video games to generate responsive, adaptive or „intelligent“ behaviors primarily in nonplayer characters (NPC’s). This so called „game AI“ focusses in general on small, relatively easy to solve tasks with the objective to improve the game-player experience. In contrast, academic AI is often used „to advance human understanding"<ref name=":1">Funge, John David: Artificial lntelligence for Computer Games. An Introduction, Massachusetts 2004, Chapter 1, S.1-15.</ref> by using a general solution. Used in many modern video games, game AI is most often implemented by using techniques such as pathfinding and decision trees to guide the actions of the NPC’s.

==Main Part==

===How to be intelligent for the player===
Although AI does not need to be personalized, most referrals to AI in video games are made to computer-controlled NPCs. But how does the player perceive the intelligence of an AI? They consider several aspects like goal-related behavior, physical characteristics, language cues and social skills. "A good looking and sympathetic NPC is likely to be considered more intelligent"<ref name=":2">Nareyek, Alexander: AI in Computer Games. In: ACM (Hrsg.): ACM Queue, Game Development. Vol. 1, No. 10, 02.2004, S.59-65.</ref> John David Funge puts it this way: <blockquote>"In terms of the player's perception of a game's AI, appropriate animations can also make an enonnous difference. For example, consider an NPC who is surrounded by hostile monsters and cannot think of anything intelligent to do. The NPC could simply stand there looking dumb, or the animation system can be triggered to play an animation of the NPC running around screaming. It is surprising how an appropriate animation can make all the difference in the player's perception of the AI."<ref name=":1" /></blockquote>Apart from an appropriate behavior of the NPC in complex situations, the aim of the AI is basically not to be better than the player, but to be as credible and entertaining as possible for them. This goal is mostly implicit. Instead, the NPC follows the lower level task of stopping the player at all costs.<ref name=":2" /> "Measures such as cheating are a lutely acceptable as long as the “suspension of disbelief” is retained."<ref name=":2" />
===Algorithms===
The basics of seemingly intelligent movement for game AI are not getting stuck in anything on their way and taking a possibly short route to the destination. To archive this goal, a couple of tools are available for development. The functionality of some popular pathfinding algorithms, including A*, can be tested on "PathFinding.js"<ref>PathFinding.js: https://qiao.github.io/PathFinding.js/visual/ (last checked on 14.04.2020)</ref>
[[File:Pathfinding and Steering.png|thumb]]

====Pathfinding with A*====
A* is the most used algorithm to compute a long distance route for NPCs. It requires the definition of waypoints and their connection to a network that spans the entire map to enable the evaluation of a route from A to B. Given a starting point and an end point, the A* algorithm gradually tries to find the shortest route along the waypoints. Step by step the algorithm explores the waypoints in increasing distance to the starting point until the end point is reached. Thereby A* uses an evaluation component which generates an estimate of the distance between a point and the destination to concentrate the spread on the most promising waypoints.<ref name=":2" /> (Shown in Fig.1)

"In many cases, a game applies pathfinding techniques at multiple granularity levels. For example, for long distances, a path of high granularity is computed first, and then the paths between the selected waypoints are computed with finer granularity."<ref name=":2" />
====Finite state Machines and Decision Trees====
Both finite state machines (FSM) and decision trees can be realized by using simple if-then statements and are both used to control the behavior of an NPC in specific situation based on environmental events. "FSM describe under which events/conditions a current state is to be replaced by another—for example, switching from an attack mode to an escape mode if the NPC is hit."<ref name=":2" />

Decision trees use a branch and leaf structure, which, starting from one point, is always divided into two further branches and thus enables decision making. "This type is often used to make high-level strategic decisions–for example, if a computer guided opponent in a strategy game should prepare an attack or concentrate on resource gathering."<ref name=":2" />

====Expert systems====
One way to create high-level behavior in games, more in the style of academic AI, is to use brute force to create a controller that has a lot of knowledge about behavior in a specific game world. This expert system suffers at the beginning from the enormous amount of situations that may occur, which inevitably leads to many of the possibilities being overlooked. This in turn causes the NPC to behave inappropriately if one of the unexpected cases occurs, causing the controlled NPC to behave stupidly. If one of these errors is found before the game is released, it can be easily fixed by adding new information about the unexpected event to the controller. For this reason it is very difficult to establish such an AI system in a complex game world and without much time effort this approach is only possible in "toy problems". One advantage is that AI does not have to be perfect in games. The game can already be launched when the process of adding new knowledge does not result in any significant changes.<ref name=":1" />

===The problem with the term (artificial) intelligence===
Artificial intelligence in computer games or, computer sciences in general, has been a topic for decades. However oftentimes finding a clear definition for what exactly artificial intelligence is and what isn't, often proves to be an issue. The problem begins with the term of intelligence itself, and this has been criticized as early as in the 70s, when Joseph Weizenbaum described intelligence as a meaningless term that is in need of a frame of reference.<ref>Weizenbaum, Joseph: ''Computer Power and Human Reason. From Judgment to Calculation'', New York 1976, Kapitel 9 [dt.: Die Macht der Computer und die Ohnmacht der Vernunft, Frankfurt/ M. 1994]. (S. 271)</ref> In the case of AI this frame of reference would be our own human intelligence, which poses the question: What is human intelligence?

As it turns out the answer to this question isn't really straightforward. It is argued, that intelligence constitutes itself from a socio-intelligent territory, that lies somewhere in between humans and their cultural techniques.<ref>Gramelsberger et al: ''Mind the Game. Die Exteriorisierung des Geistes ins Spiel gebracht'', in: Gesellschaft für Medienwissenschaft (Hg.): Zeitschrift für Medienwissenschaft. Heft 21: Künstliche Intelligenzen, Jg. 11 (2019), S.29-38.</ref> Taking into consideration, that humans and cultures differ from each other and also more than likely change over time, this means that there isn't really a clear-cut way to describe intelligence. Again, it needs a frame of reference.

While the Turing test provides a universally acclaimed method to measure the intelligence of machines, by letting a human interact with a machine. If that human is unable to distinguish the interaction with that with another human the test is passed. This test however proves to be a wrong frame of reference for AI in video games specifically, as the goal of AI in video games is not necessarily to emulate human-like behavior, but to provide the player with a fun gaming experience.<ref name=":0">Dill, Kevin: What Is Game AI?. In: CRC Press (Hrsg.): ''Game AI Pro. Collected Wisdom of Game AI Professionals'', 2014.</ref>
==Conclusion==
The constantly increasing processor power inspires the hope for an increasing performance of AI in general and Game AI in particular. With more available power for calculating AI, there is the possibility for an increased use of Expert Systems in games and therefore new possibilities.

==Related Links/Research==
[[Category:Research Approaches]]
<references />
__FORCETOC__

Computer Game AI

2021-01-18T11:50:47Z

Lena.kamenzin: Added a phrase with link to Deep Blue.

Computer Game AI is the use of Artificial Intelligence in computer games in a broader sense, that is, "to create a compelling experience for the player."<ref name=":0" /> The first Computer Game AI ever to beat a human player in a specific game is the chess computer [[Deep Blue]].

==Introduction==
Artificial Intelligence (AI) is used in video games to generate responsive, adaptive or „intelligent“ behaviors primarily in nonplayer characters (NPC’s). This so called „game AI“ focusses in general on small, relatively easy to solve tasks with the objective to improve the game-player experience. In contrast, academic AI is often used „to advance human understanding"<ref name=":1">Funge, John David: Artificial lntelligence for Computer Games. An Introduction, Massachusetts 2004, Chapter 1, S.1-15.</ref> by using a general solution. Used in many modern video games, game AI is most often implemented by using techniques such as pathfinding and decision trees to guide the actions of the NPC’s.

==Main Part==

===How to be intelligent for the player===
Although AI does not need to be personalized, most referrals to AI in video games are made to computer-controlled NPCs. But how does the player perceive the intelligence of an AI? They consider several aspects like goal-related behavior, physical characteristics, language cues and social skills. "A good looking and sympathetic NPC is likely to be considered more intelligent"<ref name=":2">Nareyek, Alexander: AI in Computer Games. In: ACM (Hrsg.): ACM Queue, Game Development. Vol. 1, No. 10, 02.2004, S.59-65.</ref> John David Funge puts it this way: <blockquote>"In terms of the player's perception of a game's AI, appropriate animations can also make an enonnous difference. For example, consider an NPC who is surrounded by hostile monsters and cannot think of anything intelligent to do. The NPC could simply stand there looking dumb, or the animation system can be triggered to play an animation of the NPC running around screaming. It is surprising how an appropriate animation can make all the difference in the player's perception of the AI."<ref name=":1" /></blockquote>Apart from an appropriate behavior of the NPC in complex situations, the aim of the AI is basically not to be better than the player, but to be as credible and entertaining as possible for them. This goal is mostly implicit. Instead, the NPC follows the lower level task of stopping the player at all costs.<ref name=":2" /> "Measures such as cheating are a lutely acceptable as long as the “suspension of disbelief” is retained."<ref name=":2" />
===Algorithms===
The basics of seemingly intelligent movement for game AI are not getting stuck in anything on their way and taking a possibly short route to the destination. To archive this goal, a couple of tools are available for development. The functionality of some popular pathfinding algorithms, including A*, can be tested on "PathFinding.js"<ref>PathFinding.js: https://qiao.github.io/PathFinding.js/visual/ (last checked on 14.04.2020)</ref>
[[File:Pathfinding and Steering.png|thumb]]

====Pathfinding with A*====
A* is the most used algorithm to compute a long distance route for NPCs. It requires the definition of waypoints and their connection to a network that spans the entire map to enable the evaluation of a route from A to B. Given a starting point and an end point, the A* algorithm gradually tries to find the shortest route along the waypoints. Step by step the algorithm explores the waypoints in increasing distance to the starting point until the end point is reached. Thereby A* uses an evaluation component which generates an estimate of the distance between a point and the destination to concentrate the spread on the most promising waypoints.<ref name=":2" /> (Shown in Fig.1)

"In many cases, a game applies pathfinding techniques at multiple granularity levels. For example, for long distances, a path of high granularity is computed first, and then the paths between the selected waypoints are computed with finer granularity."<ref name=":2" />
====Finite state Machines and Decision Trees====
Both finite state machines (FSM) and decision trees can be realized by using simple if-then statements and are both used to control the behavior of an NPC in specific situation based on environmental events. "FSM describe under which events/conditions a current state is to be replaced by another—for example, switching from an attack mode to an escape mode if the NPC is hit."<ref name=":2" />

Decision trees use a branch and leaf structure, which, starting from one point, is always divided into two further branches and thus enables decision making. "This type is often used to make high-level strategic decisions–for example, if a computer guided opponent in a strategy game should prepare an attack or concentrate on resource gathering."<ref name=":2" />

====Expert systems====
One way to create high-level behavior in games, more in the style of academic AI, is to use brute force to create a controller that has a lot of knowledge about behavior in a specific game world. This expert system suffers at the beginning from the enormous amount of situations that may occur, which inevitably leads to many of the possibilities being overlooked. This in turn causes the NPC to behave inappropriately if one of the unexpected cases occurs, causing the controlled NPC to behave stupidly. If one of these errors is found before the game is released, it can be easily fixed by adding new information about the unexpected event to the controller. For this reason it is very difficult to establish such an AI system in a complex game world and without much time effort this approach is only possible in "toy problems". One advantage is that AI does not have to be perfect in games. The game can already be launched when the process of adding new knowledge does not result in any significant changes.<ref name=":1" />

===The problem with the term (artificial) intelligence===
Artificial intelligence in computer games or, computer sciences in general, has been a topic for decades. However oftentimes finding a clear definition for what exactly artificial intelligence is and what isn't, often proves to be an issue. The problem begins with the term of intelligence itself, and this has been criticized as early as in the 70s, when Joseph Weizenbaum described intelligence as a meaningless term that is in need of a frame of reference.<ref>Weizenbaum, Joseph: ''Computer Power and Human Reason. From Judgment to Calculation'', New York 1976, Kapitel 9 [dt.: Die Macht der Computer und die Ohnmacht der Vernunft, Frankfurt/ M. 1994]. (S. 271)</ref> In the case of AI this frame of reference would be our own human intelligence, which poses the question: What is human intelligence?

As it turns out the answer to this question isn't really straightforward. It is argued, that intelligence constitutes itself from a socio-intelligent territory, that lies somewhere in between humans and their cultural techniques.<ref>Gramelsberger et al: ''Mind the Game. Die Exteriorisierung des Geistes ins Spiel gebracht'', in: Gesellschaft für Medienwissenschaft (Hg.): Zeitschrift für Medienwissenschaft. Heft 21: Künstliche Intelligenzen, Jg. 11 (2019), S.29-38.</ref> Taking into consideration, that humans and cultures differ from each other and also more than likely change over time, this means that there isn't really a clear-cut way to describe intelligence. Again, it needs a frame of reference.

While the Turing test provides a universally acclaimed method to measure the intelligence of machines, by letting a human interact with a machine. If that human is unable to distinguish the interaction with that with another human the test is passed. This test however proves to be a wrong frame of reference for AI in video games specifically, as the goal of AI in video games is not necessarily to emulate human-like behavior, but to provide the player with a fun gaming experience.<ref name=":0">Dill, Kevin: What Is Game AI?. In: CRC Press (Hrsg.): ''Game AI Pro. Collected Wisdom of Game AI Professionals'', 2014.</ref>
==Conclusion==
The constantly increasing processor power inspires the hope for an increasing performance of AI in general and Game AI in particular. With more available power for calculating AI, there is the possibility for an increased use of Expert Systems in games and therefore new possibilities.

==Related Links/Research==
[[Category:Research Approaches]]
<references />
__FORCETOC__

Deep blue

2020-04-10T13:27:40Z

Lena.kamenzin: added paragraphs

==Introduction==

Deep Blue is a chess computer ([[Computer Game AI]]) that beat a human player for the first time in a chess world championship tournament in 1997. It is based on the Brute Force Method or A-strategy. After the victory over world chess champion Garri Kasparov, a broad public discourse on the effects of artificial intelligence ensued. In addition, the question as to what extent Deep Blue and other chess computers can be considered intelligent arose for research.

This whole article is based on the text by Martina Heßler: „Der Erfolg der ‚Dummheit‘“, 2017.

==Main Part==

Initially, research on AI was intended to gain new insights into the human brain and human thinking. It should thus be possible to understand and map thought patterns. To achieve this goal, two strategies were possible: The A strategy, which relies on high computing power, and the B strategy, whose strength is selective problem solving, but its implementation is significantly more complex. In the case of Deep Blue - and the research of the following years - the A strategy, or Brute Force method, was chosen. This is a mechanical procedure that calculates all possible moves in a game of chess without distinguishing between useful and meaningless move options. This procedure "is based on a search algorithm, an evaluation function of the moves and a huge database with played games"(Heßler: p.11).

During their development, it was not intended that chess computers like Deep Blue would deal exclusively with chess. Chess was indeed considered THE characteristic of human intelligence, which is why the programmers dealt with the game in the first place. However, the shift to optimizing chess skills, rather than gaining knowledge through chess, met with criticism in the research field. Deep Blue had lost its original purpose - even though, by defeating Kasparov, it crossed a threshold of machine intelligence in the history of computer research.

Just like the question relating to / concerning the purpose of concentrating on chess, the question arises as to whether one can actually speak of intelligence in Deep Blue or whether one can represent human thinking with machines at all.

Hubert Dreyfuß supported/represented the view that some characteristics of human thinking could not be simulated by machines: for example, a computer was not capable of understanding language in all its implicit and performative dimensions, of distinguishing the important from the unimportant or of coping with the complexity of everyday human life. Instead of being able to master everyday life, Deep Blue, for example, is only capable of playing chess - very well, but nothing beyond that. In terms of chess, in which Deep Blue achieved outstanding results, it lacked essential human characteristics: "imagination, ability to combine, acumen, wit, courage, caution". (Geppert: p. 41 f.) Basically, the computer lacked emotion and gut feeling. According to Hessler, the abstract, logical thinking of a computer cannot be equated with human intelligence. In addition, the machine lacks reflexive abilities. It cannot justify moves that have been made in the game that are not comprehensible to humans. Based on this argumentation, there is the thesis that Deep Blue is stupid despite its performance: It is not actual intelligence, but behaviour that merely appears intelligent.

Through the victory over the chess world champion, Deep Blue was briefly attributed a kind of intelligence, which however, is revised again almost at the same moment. In addition, the game of chess is suffering a loss of reputation and is no longer considered an intelligent game. The moment in which a chess computer could have been considered intelligent is nullified by the change in parameters.

The alternative method of the B strategy might have come closer to the concept of human thinking. This is a selective strategy in which senseless moves are first recognised and the next move is decided on a situational basis. The similarity to human thinking lies not only in the networked structures but also in the selective way of working.

The next generation after Deep Blue was Watson, which made a famous appearance in the quiz show "Jeopardy!" in 2001. Watson sees itself as a learning system, owns databases and searches thousands of documents to analyse unstructured data. This leads to an understanding of ambiguity and thus helps it to correctly answer trick questions in the quiz show. Compared to its predecessor, it succeeds in recognizing and interpreting contexts on a statistical basis.

The questions that the text finally raises deal with the possibilities of interaction between humans and AI, as well as whether intelligence is the only means to successfully win a chess game or whether pure logic and computing power are sufficient. Hessler remarked that the moves that seem "stupid" are often the ones that lead to success.

==Conclusion==

Despite clear objectives, AI research ended up in a dead end. Instead of devoting its resources to gaining knowledge about the human brain, research drifted into the field of computer applications in chess and industry. Nevertheless, it did raise questions about the concept of human, artificial and machine intelligence. Deep Blue was regarded as intelligent because of its chess skills, but at the same time it also distanced itself from them. Furthermore, it and its history caused discussions in media philosophy as to whether artificial intelligence is or will ever be possible at all. Even if research did not follow its original path, it was nevertheless ground-breaking for today's (further) development in the field of computer research and artificial intelligence.

==Related Links/Research==

Heßler, Martina: Der Erfolg der »Dummheit«. Deep Blues Sieg über den Schach-weltmeister Garri Kasparov und der Streit über seine Bedeutung für die Künstliche Intelligenz-Forschung, 2017 online publiziert.

Deep blue

2020-04-10T13:21:26Z

Lena.kamenzin:

== Introduction ==

Deep Blue is a chess computer ([[Computer Game AI]]) that beat a human player for the first time in a chess world championship tournament in 1997. It is based on the Brute Force Method or A-strategy. After the victory over world chess champion Garri Kasparov, a broad public discourse on the effects of artificial intelligence ensued. In addition, the question as to what extent Deep Blue and other chess computers can be considered intelligent arose for research.

This whole article is based on the text by Martina Heßler: „Der Erfolg der ‚Dummheit‘“, 2017.

== Main Part ==

Initially, research on AI was intended to gain new insights into the human brain and human thinking. It should thus be possible to understand and map thought patterns. To achieve this goal, two strategies were possible: The A strategy, which relies on high computing power, and the B strategy, whose strength is selective problem solving, but its implementation is significantly more complex. In the case of Deep Blue - and the research of the following years - the A strategy, or Brute Force method, was chosen. This is a mechanical procedure that calculates all possible moves in a game of chess without distinguishing between useful and meaningless move options. This procedure "is based on a search algorithm, an evaluation function of the moves and a huge database with played games"(Heßler: p.11).

During their development, it was not intended that chess computers like Deep Blue would deal exclusively with chess. Chess was indeed considered THE characteristic of human intelligence, which is why the programmers dealt with the game in the first place. However, the shift to optimizing chess skills, rather than gaining knowledge through chess, met with criticism in the research field. Deep Blue had lost its original purpose - even though, by defeating Kasparov, it crossed a threshold of machine intelligence in the history of computer research.
Just like the question relating to / concerning the purpose of concentrating on chess, the question arises as to whether one can actually speak of intelligence in Deep Blue or whether one can represent human thinking with machines at all.
Hubert Dreyfuß supported/represented the view that some characteristics of human thinking could not be simulated by machines: for example, a computer was not capable of understanding language in all its implicit and performative dimensions, of distinguishing the important from the unimportant or of coping with the complexity of everyday human life. Instead of being able to master everyday life, Deep Blue, for example, is only capable of playing chess - very well, but nothing beyond that. In terms of chess, in which Deep Blue achieved outstanding results, it lacked essential human characteristics: "imagination, ability to combine, acumen, wit, courage, caution". (Geppert: p. 41 f.) Basically, the computer lacked emotion and gut feeling. According to Hessler, the abstract, logical thinking of a computer cannot be equated with human intelligence. In addition, the machine lacks reflexive abilities. It cannot justify moves that have been made in the game that are not comprehensible to humans. Based on this argumentation, there is the thesis that Deep Blue is stupid despite its performance: It is not actual intelligence, but behaviour that merely appears intelligent.
Through the victory over the chess world champion, Deep Blue was briefly attributed a kind of intelligence, which however, is revised again almost at the same moment. In addition, the game of chess is suffering a loss of reputation and is no longer considered an intelligent game. The moment in which a chess computer could have been considered intelligent is nullified by the change in parameters.
The alternative method of the B strategy might have come closer to the concept of human thinking. This is a selective strategy in which senseless moves are first recognised and the next move is decided on a situational basis. The similarity to human thinking lies not only in the networked structures but also in the selective way of working.
The next generation after Deep Blue was Watson, which made a famous appearance in the quiz show "Jeopardy!" in 2001. Watson sees itself as a learning system, owns databases and searches thousands of documents to analyse unstructured data. This leads to an understanding of ambiguity and thus helps it to correctly answer trick questions in the quiz show. Compared to its predecessor, it succeeds in recognizing and interpreting contexts on a statistical basis.
The questions that the text finally raises deal with the possibilities of interaction between humans and AI, as well as whether intelligence is the only means to successfully win a chess game or whether pure logic and computing power are sufficient. Hessler remarked that the moves that seem "stupid" are often the ones that lead to success.

== Conclusion ==

Despite clear objectives, AI research ended up in a dead end. Instead of devoting its resources to gaining knowledge about the human brain, research drifted into the field of computer applications in chess and industry. Nevertheless, it did raise questions about the concept of human, artificial and machine intelligence. Deep Blue was regarded as intelligent because of its chess skills, but at the same time it also distanced itself from them. Furthermore, it and its history caused discussions in media philosophy as to whether artificial intelligence is or will ever be possible at all. Even if research did not follow its original path, it was nevertheless ground-breaking for today's (further) development in the field of computer research and artificial intelligence.

== Related Links/Research ==

Heßler, Martina: Der Erfolg der »Dummheit«. Deep Blues Sieg über den Schach-weltmeister Garri Kasparov und der Streit über seine Bedeutung für die Künstliche Intelligenz-Forschung, 2017 online publiziert.

Deep blue

2020-04-10T13:15:44Z

Lena.kamenzin: Created page with "== Introduction == Deep Blue is a chess computer that beat a human player for the first time in a chess world championship tournament in 1997. It is based on the Brute Force..."

== Introduction ==

Deep Blue is a chess computer that beat a human player for the first time in a chess world championship tournament in 1997. It is based on the Brute Force Method or A-strategy. After the victory over world chess champion Garri Kasparov, a broad public discourse on the effects of artificial intelligence ensued. In addition, the question as to what extent Deep Blue and other chess computers can be considered intelligent arose for research.

This whole article is based on the text by Martina Heßler: „Der Erfolg der ‚Dummheit‘“, 2017.

== Main Part ==

Initially, research on AI was intended to gain new insights into the human brain and human thinking. It should thus be possible to understand and map thought patterns. To achieve this goal, two strategies were possible: The A strategy, which relies on high computing power, and the B strategy, whose strength is selective problem solving, but its implementation is significantly more complex. In the case of Deep Blue - and the research of the following years - the A strategy, or Brute Force method, was chosen. This is a mechanical procedure that calculates all possible moves in a game of chess without distinguishing between useful and meaningless move options. This procedure "is based on a search algorithm, an evaluation function of the moves and a huge database with played games"(Heßler: p.11).

During their development, it was not intended that chess computers like Deep Blue would deal exclusively with chess. Chess was indeed considered THE characteristic of human intelligence, which is why the programmers dealt with the game in the first place. However, the shift to optimizing chess skills, rather than gaining knowledge through chess, met with criticism in the research field. Deep Blue had lost its original purpose - even though, by defeating Kasparov, it crossed a threshold of machine intelligence in the history of computer research.
Just like the question relating to / concerning the purpose of concentrating on chess, the question arises as to whether one can actually speak of intelligence in Deep Blue or whether one can represent human thinking with machines at all.
Hubert Dreyfuß supported/represented the view that some characteristics of human thinking could not be simulated by machines: for example, a computer was not capable of understanding language in all its implicit and performative dimensions, of distinguishing the important from the unimportant or of coping with the complexity of everyday human life. Instead of being able to master everyday life, Deep Blue, for example, is only capable of playing chess - very well, but nothing beyond that. In terms of chess, in which Deep Blue achieved outstanding results, it lacked essential human characteristics: "imagination, ability to combine, acumen, wit, courage, caution". (Geppert: p. 41 f.) Basically, the computer lacked emotion and gut feeling. According to Hessler, the abstract, logical thinking of a computer cannot be equated with human intelligence. In addition, the machine lacks reflexive abilities. It cannot justify moves that have been made in the game that are not comprehensible to humans. Based on this argumentation, there is the thesis that Deep Blue is stupid despite its performance: It is not actual intelligence, but behaviour that merely appears intelligent.
Through the victory over the chess world champion, Deep Blue was briefly attributed a kind of intelligence, which however, is revised again almost at the same moment. In addition, the game of chess is suffering a loss of reputation and is no longer considered an intelligent game. The moment in which a chess computer could have been considered intelligent is nullified by the change in parameters.
The alternative method of the B strategy might have come closer to the concept of human thinking. This is a selective strategy in which senseless moves are first recognised and the next move is decided on a situational basis. The similarity to human thinking lies not only in the networked structures but also in the selective way of working.
The next generation after Deep Blue was Watson, which made a famous appearance in the quiz show "Jeopardy!" in 2001. Watson sees itself as a learning system, owns databases and searches thousands of documents to analyse unstructured data. This leads to an understanding of ambiguity and thus helps it to correctly answer trick questions in the quiz show. Compared to its predecessor, it succeeds in recognizing and interpreting contexts on a statistical basis.
The questions that the text finally raises deal with the possibilities of interaction between humans and AI, as well as whether intelligence is the only means to successfully win a chess game or whether pure logic and computing power are sufficient. Hessler remarked that the moves that seem "stupid" are often the ones that lead to success.

== Conclusion ==

Despite clear objectives, AI research ended up in a dead end. Instead of devoting its resources to gaining knowledge about the human brain, research drifted into the field of computer applications in chess and industry. Nevertheless, it did raise questions about the concept of human, artificial and machine intelligence. Deep Blue was regarded as intelligent because of its chess skills, but at the same time it also distanced itself from them. Furthermore, it and its history caused discussions in media philosophy as to whether artificial intelligence is or will ever be possible at all. Even if research did not follow its original path, it was nevertheless ground-breaking for today's (further) development in the field of computer research and artificial intelligence.

== Related Links/Research ==

Heßler, Martina: Der Erfolg der »Dummheit«. Deep Blues Sieg über den Schach-weltmeister Garri Kasparov und der Streit über seine Bedeutung für die Künstliche Intelligenz-Forschung, 2017 online publiziert.

Computer Game AI

2020-04-02T12:25:52Z

Lena.kamenzin: added new link: "Deep Blue"

[Theory Building]

Computer Game AI is the use of Artificial Intelligence in computer games in a broader sense, that is, "to create a compelling experience for the player." (Dill 3)

Cross references:

[[Metagaming]]

[[Deep Blue]]
[[Category:Research Approaches]]