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CS B551: Elements of
Artificial Intelligence
Review
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Major Themes
Philosophy of AI
Intelligent agents
Applications and variations
of search
Planning
Game playing
Search:
Motion planning
the foundation of
decision-making
Reasoning with uncertainty:
Our representations of
the real world are imperfect
Machine learning:
Making sense of massive
amounts of data
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Philosophy of AI
• AI is an attempt of reproducing human
reasoning and intelligent behavior by
computational methods
• Turing Test
• Searle’s Chinese room
• Agent frameworks
– Reactive, deliberative, learning
– Environment observability
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Search
• Systematic way of exploring alternatives
• State space, successor function
• Blind search: DFS, BFS, ID
– Complexity
– Revisited states
– Nonuniform costs
• Informed search: A*
– Admissible and consistent heuristics
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Search Applications and
Variants
• Planning: describe search problems in a
common language
– STRIPS
– Planning graph, backward chaining
• Game playing
– Minimax
– Alpha-beta pruning
• Motion planning
– Configuration space
– Discretization is key
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Reasoning With Uncertainty
• Nondeterministic uncertainty
– AND/OR trees
– Belief states
• Probabilistic uncertainty
– Bellman equation
– Value iteration, policy iteration
• Bayesian reasoning
– Joint/conditional distributions
– Bayesian networks and Markov models
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Machine Learning
• Statistical learning
– Bayesian parameter learning
– Maximum likelihood, MAP
• Techniques: decision trees, neural
networks, support vector machines,
boosting
• Issues: overfitting/generalization, learning
time, cross-validation
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Reminder
• Final project mid-term report
– 1-2 paragraphs
• Next week: project presentations
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Example
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B551 Final Project Example:
Fast Collision-Free Trajectory Optimization
Using Time-Optimal Shortcuts
Kris Hauser
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Issue: Jerky Paths Produced by
PRM Motion Planners
• PRMs can quickly produce collisionfree motion in high-D spaces, but do
not optimize smoothness of path
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Project Idea: Smooth
Shortcutting
• Shortcutting is a common postprocessing
technique
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Project Idea: Smooth
Shortcutting
• Shortcutting is a common postprocessing
technique
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Project Idea: Smooth
Shortcutting
• Shortcutting is a common postprocessing
technique
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Project Idea: Smooth
Shortcutting
• Shortcutting is a common postprocessing
technique
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Project Idea: Smooth
Shortcutting
• Shortcutting is a common postprocessing
technique
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Project Idea: Smooth
Shortcutting
• Shortcutting is a common postprocessing
technique
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Project Idea: Smooth
Shortcutting
• Shortcutting is a common postprocessing
technique
• How about using smooth shortcuts?
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Time-Optimal Bounded
Velocity/Acceleration
Trajectories
• Min-time trajectory
– Specified start/end point, velocity
– Composed of up to 2 parabolic + 1 linear
segments
Accel amax
Decel amax
Hit vmax
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Shortcutting Algorithm
• Issues:
• Collision checking (bounding volume
hierarchy, branch+bound)
• Stopping criterion
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Results
Original 6-milestone
trajectory
Duration 5.7s
Maybe learn a
good stopping
point?
Smoothed trajectory
Duration 3.1s
Computed in ~2.8s
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