implement the MDP model with temporal abstraction
add the updated results of MDP with temporal decisions
a few minor changes
include AMPL data file I/O interface for MATLAB

1) amplwrite.m is written by Che-Lin Su.
2) glpkread.m is written by Arthur Richards.
use 1x constant travel time
export the choice probability to a .m file

also adjust the floating-point number output precision
solve the component MDP model separately

EV values from the component MDP will be used as
lower and upper bounds in the composite MDP model
clean up set names
define the longest duration for a joint decision

Thus, the joint decision space is greatly reduced,
otherwise AMPL runs out of memory.
solve the household scheduling problem

1) this change directly solves the composite MDP
2) the results match with the component MDPs when rho is set to zero
add the Bellman equations for the L/U bounds
solve the composite MDP directly without dynamic merging

1) clean up the main model file MarkovActv.mod and fix in Bellman eqn.
2) reformat the exported .m data files
3) update the results in DATA/ (EUtil.m & jointEUtil.m)
clean up AMPL source code filenames

1) solving MDPs starts with MDP... or JointMDP...
2) estimating model parameters starts with MLE...
update source code information
re-run component and composite MDPs with beta = 0.9
blank files for dynamic programming
change all the fixed var to param

slightly different resutls are obtained (see EUtil.m & jointEUtil.m)
remove files in DATA/ and FIGURES/
use Bell-shaped temporal utility function
any decision should have a duration logner than 1

1) otherwise lots of dummy decisions with zero duration are
generated and the decision space is increasingly large
2) also only the decisions with duration between 1 and DH
are exported into MATLAB