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Explain what the constraints are, generally spend
more time on this slide.
Explain what the constraints are, generally spend
more time on this slide.
Explain what the constraints are, generally spend
more time on this slide.
Explain what the constraints are, generally spend
more time on this slide.
Explain what the constraints are, generally spend
more time on this slide.
To track the n most likely trajectories
For each new time step until the end of time
Create full copy of the model for the new time step
Assign command and observation variables
Until n consistent trajectories found
Enumerate the next best t-length trajectory
Throw it out if inconsistent
Report the t-length trajectories
An Algorithm with Only Two Problems!
Space  - Representation grows linearly with time
Time   - Search space grows exponentially with time                   - Checks an exponential number of obviously wrong candidates
The assignments to t capture every possible trajectory
Trajectories can be enumerated in prior probability order
P(trajectory) = S P(t assignment)
Each trajectory can be checked for agreement with observations
Avoid committing to a small number of trajectories
Build a structure that compactly represents all evolutions
Generate additional trajectories in order as needed
To track the n most likely trajectories
For each new time step until the end of time
Create full copy of the model for the new time step
Assign command and observation variables
Until n consistent trajectories found
Enumerate the next best t-length trajectory
Throw it out if inconsistent
Report the t-length trajectories
An Algorithm with Only Two Problems!
Space  - Representation grows linearly with time
Time   - Search space grows exponentially with time                   - Checks an exponential number of obviously wrong candidates
The assignments to t capture every possible trajectory
Trajectories can be enumerated in prior probability order
P(trajectory) = S P(t assignment)
Each trajectory can be checked for agreement with observations
Avoid committing to a small number of trajectories
Build a structure that compactly represents all evolutions
Generate additional trajectories in order as needed
To track the n most likely trajectories
For each new time step until the end of time
Create full copy of the model for the new time step
Assign command and observation variables
Until n consistent trajectories found
Enumerate the next best t-length trajectory
Throw it out if inconsistent
Report the t-length trajectories
An Algorithm with Only Two Problems!
Space  - Representation grows linearly with time
Time   - Search space grows exponentially with time                   - Checks an exponential number of obviously wrong candidates
The assignments to t capture every possible trajectory
Trajectories can be enumerated in prior probability order
P(trajectory) = S P(t assignment)
Each trajectory can be checked for agreement with observations
Avoid committing to a small number of trajectories
Build a structure that compactly represents all evolutions
Generate additional trajectories in order as needed
To track the n most likely trajectories
For each new time step until the end of time
Create full copy of the model for the new time step
Assign command and observation variables
Until n consistent trajectories found
Enumerate the next best t-length trajectory
Throw it out if inconsistent
Report the t-length trajectories
An Algorithm with Only Two Problems!
Space  - Representation grows linearly with time
Time   - Search space grows exponentially with time                   - Checks an exponential number of obviously wrong candidates
The assignments to t capture every possible trajectory
Trajectories can be enumerated in prior probability order
P(trajectory) = S P(t assignment)
Each trajectory can be checked for agreement with observations
Avoid committing to a small number of trajectories
Build a structure that compactly represents all evolutions
Generate additional trajectories in order as needed