class: center, middle

Gerard Escudero, 2020

.footnote[Source: inverse ]

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• .cyan[Artificial Intelligence]

• Game AI

• Content & Evaluation

• References & Tools

• an agent does tasks normally attached to humans (or animals)

• an agent has AI when it exhibits "smart" features:

  • learning, reasoning, behavior, reaction

• play chess

• reading license plates

• weather forecasting

.cols5050[ .col1[

• Early Days: phylosofy
  Turing test (1950)

• 1950: symbolic era
  Knowledge set + resoning

• 1960: cybernetics
  biological (neural networks)

• 1966: crisis!
  ALPAC report against MT

• 1970: cybernetics (MLP)
  symbolic (Search Strategies, Uncertainty)

• 1980: real applications
  expert systems, fuzzy logic ] .col2[

• 1990-2000: machine learning (statistical)

• 2010-: deep learning ]]

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• .brown[Artificial Intelligence]

• .cyan[Game AI]

• Content & Evaluation

• References & Tools

• 1980: Pac-Man (state machine)

• 1994: Warcraft (pathfinding)

• 1997: Goldeneye 007 (sense simulation)

• 1997: Creatures (AI centered game)

• 1998: Warhammer: Dark Omen (formation motion)

• 1998: Half-Life (AI collaborators)

• 2000: The Sims (AI centered game)

• 2001: Halo (Decision Trees)

• 2001: Black & White (AI centered game)

• 2005: F.E.A.R. (GOAP)

• 2015: AlphaGo (Deep Learning)

Building a game experience should be our main objective.

.blue[The Complexity Fallacy]:

• Select always the simplest way to give the illusion of complexity

• .blue[Occam's Razor]: the simplest explanation is most likely the right one

.blue[Perception window]:

• Interaction with agents lasts a very short time.

• Try to avoid commonsense behavioral changes
  (see 2on video at references).

.blue[Memory & speed restrictions]

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• .brown[Artificial Intelligence]

• .brown[Game AI]

• .cyan[Content & Evaluation]

• References & Tools

.blue[Main concepts]:

• Movement (steering behaviours)

• Planning for movement (pathfinding)

• Decision Taking (behavior trees)

• Machine Learning (neural networks and deep learning)

• AI Game Design

.blue[Goals]:

• Game AI: common tools

• Unity & C#

• .blue[Steering Behaviours]

  • Wander & Hide
  • Flocking

• .blue[Navigation Mesh & Pathfinding]

• .blue[World Interface] (senses)

• .blue[Decision Making]

  • Behaviour Trees
  • Smart Objects & Planners

• .blue[Group tactical movement and decision taking]

  • Tactical group movement & Blackboards for information sharing
  • Behavior Trees for group coordination

• .blue[Machine Learning]

  • Deep Learning
  • Reinforced Learning

.cols5050[ .col1[

• Counts as 20%
• November 1st at 23:59
• Movement: steerings & pathfinding

• Counts as 20%
• December 8th at 23:59
• Behaviour: Behaviour Trees

• Counts as 25%
• January 10th at 23:59
• Machine Learning ] .col2[

• Work is in teams of 2

• Counts as 10%

• Counts as 25%
• January

• Counts as 25%
• Max grade is 5
• January 30th - February 5th ]]

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• .brown[Artificial Intelligence]

• .brown[Game AI]

• .brown[Content & Evaluation]

• .cyan[References & Tools]

• Ian Millington. AI for Games (3rd ed). CRC Press, 2019.

• Unity (personal edition)

• Github (private repositories)

.blue[See next videos]:

• Artificial Intelligence: Skyrim - The Elder Scrolls V. Ether Dynamics,

• Stupidest AI Bug Contest 2011. Masi84, 2011.

• Top 10 Video Games with the Best AI. WatchMojo.com, 2015.

• MarI/O - Machine Learning for Video Games. SethBling, 2015.

• The wonderful and terrifying implications of computers that can learn. Jeremy Howard, 2014.