ARTIFICIAL INTELLIGENCE Summary · Summary: Search & Pathplanning Usedin environments thatare...
Transcript of ARTIFICIAL INTELLIGENCE Summary · Summary: Search & Pathplanning Usedin environments thatare...
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ARTIFICIAL INTELLIGENCE
Lecturer: Silja Renooij
Summary
Utrecht University The Netherlands
These slides are part of the INFOB2KI Course Notes available fromwww.cs.uu.nl/docs/vakken/b2ki/schema.html
INFOB2KI 2019-2020
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Subject overview Introduction (chapters 1,2) Pathfinding & Search (a.o. chapter 4) Learning (a.o. chapter 7) Deterministic planning & decision making (a.o. chapter 5, 6, 8, 11) Reasoning and planning under uncertainty (a.o. chapter 5) Movement (chapter 3)
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What is Intelligence? What is (game) AI?
Intelligence: A very general mental capability […] for comprehending our surroundings—"catching on," "making sense" of things, or "figuring out" what to do
Artificial Intelligence: four categories
Game AI: is about the illusion of human behaviour(Smart ‐‐ to a certain extent, unpredictable but rational, emotional influences, body language to communicate emotions, integrated in the environment)
Thinking rationally Thinking humanly
Acting rationally Acting humanly
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Summary: Search & Pathplanning Used in environments that are static, deterministic, observable, and completely known(accessible)
Goal‐based Atomic states and actions (no domain structure) Uninformed search: BFS, DFS, UCS, IDS… Informed search: Greedy, A*,… Local search: hill‐climbing, simulated annealing, GAs,…
Adversarial search: minimax (alpha‐beta)
What aboutnatural paths?
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Natural paths: Short…(?)Pre‐processing step: automatic construction of waypoint graph, using sampling
Automatic construction of roadmaps for path planning in games, Nieuwenhuisen, Kamphuis, Mooijekind & Overmars, 2004. 5
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Natural paths: enough clearance (?)Step 1: move waypoint c to ‘center’ cv
between obstacles
Idea: find closest point on obstacle move in opposite direction until
same distance to other obstacle
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Natural paths: enough clearance (?)Step 2: move edges to ‘center’ between obstacles
Idea: Split edge with insufficient clearance move middelpoint to center (green line)
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Natural paths: continuous (?)
Add a circular blend to every pair of incoming edges for every waypoint
Curvature depending on the amount of clearance
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Natural paths result
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Path planning:some problems remain…
Characters will still all follow the same path
No dynamic evasion No natural behavior Characters take turns that have no corresponding animation
In‐game changes to the characters have no effect on the path they follow Mood has no effect (although claimed by some games, there is no real evidence of it’s use) …
Openproblems
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Summary: Learning Learning is essential for intelligence Learning is difficult when the world is dynamic, large and uncertain
Reinforcement learning does not necessarily need a model of the world
Supervised techniques learn from ‘examples’– Decision trees, Naïve Bayes classifiers– Neural Networks; also used as function approximators
Evolutionary algorithms used to let strategies/solutions compete ~ search
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Summary: Uncertainty
How to reason with ‘uncertain’ information– Fuzzy logic, Bayesian networks
What is the best strategy if– The outcome of actions is uncertain– The world state is uncertain
MDP, POMDP used to determine optimal actions (planning) under uncertainty
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Summary Planning
Given initial state(s), goal(s) and set of actions: find a plan (a sequence of actions ) that is guaranteed to achieve goal(s).
Inefficient as search: complete state descriptions, too many actions, unused problem structure, multiple start and/or goal states, does plan exist?
Different planning formalisms for problems with different characteristics
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Planning: problem characteristics Discrete (+ finite?) or continuous values Fully or partially observable One or more initial states
Deterministic or stochastic With or without duration Concurrent or sequential
Static or dynamic
Reach goal state or maximize reward
Single agent or multi‐agent– Cooperative or selfish– Individual or centralized planning
States:
Actions:
Env.:
Objective:
Planners:
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Classical: STRIPS (GOAP), NOAH, HTN (SHOP)
Discrete (+ finite?) or continuous values Fully or partially observable 1 or more initial states
Deterministic or stochastic With or without duration Concurrent or sequential
Static or dynamic
Reach goal state or maximize reward
Single agent or multi‐agent– Cooperative or selfish– Individual or centralized planning
Dknown1
DWS
S
G
S
Except GOAP!
States:
Actions:
Env.:
Objective:
Planners:
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Reactive: DT, FSM, BT, rule-based, …
Discrete (+ finite?) or continuous values Fully or partially observable 1 or more initial states
Deterministic or stochastic With or without duration Concurrent or sequential
Static or dynamic
Reach goal state or maximize reward
Single agent or multi‐agent– Cooperative or selfish– Individual or centralized planning
D
S
D
S
States:
Actions:
Env.:
Objective:
Planners:
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Stochastic: MDP, POMDP
Discrete (+ finite?) or continuous values Fully or partially observable 1 or more initial states
Deterministic or stochastic With or without duration Concurrent or sequential
Static or dynamic
Reach goal state or maximize reward
Single agent or multi‐agent– Cooperative or selfish– Individual or centralized planning
Often D
≥ 1
SW
D
R
S
MDP: F, POMDP: PStates:
Actions:
Env.:
Objective:
Planners:
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Summary Moving
Individual steering behaviors– Intelligent animation?– What if an animation fails halfway?
Group movement and teamwork– Crowd simulation:
• Move together but separate• Who stops first? Where do you stop? How do you pass obstacles?
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The MIRAnim Engine (for mixed reality)Cassell et al. identify two types of communicative body motions:
J. Cassell, T. Bickmore, M. Billinghurst, L. Campbell, K. Chang, Vilhjalmsson, H., and H. Yan. Embodiment in conversational interfaces: Rea. In Proceedings of the CHI’99 Conference, pages 520–527, 1999.
Intelligent Animation
Performance Animation
Interactive Virtual Humans
Gestures Posture shifts
MIRAnim
BLENDING control
idle
ness
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Intelligent animation:
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GRETA: Embodied Conversational AgentGreta can talk and simultaneously show facial expressions, gestures, gaze, and head movements.
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Intelligent Animation: problemsTo make agent realistic/believable: Blend of facial animation and body animation Blend of moving and handling objects Adjustment of moving/posture to environment Animation shouldn’t get “stuck” Not too realistic?
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Not covered in this course
Constraint satisfaction Utility theory Natural Language Processing Vision/perception (recognizing objects) Knowledge representation (ontologies) Ethics …
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Conclusions
Much is achieved:– Watson, Deep Blue– Alpha Go (Zero)– Robocup soccer– Autonomous cars– …
With every step taken new challenges become visible! (And not just in AI)
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Interested in more? Search the internet Course page Other courses ICS dept:
– Applied Games (Ba IKU)– Kennissystemen (Ba IKU)– Intelligente Systemen (Ba ICA)– Computationele Intelligentie (Ba ICA)– Probabilistic Reasoning (Ma COSC + AI)– Evolutionary Computing (Ma COSC + AI)– AI for game technology (Ma GMT)– …
AI master (or COSC/GMT with electives)
Thank you!(please use Caracal forfeedback)
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