#
# This demonstrates assigning objects to object sets (osets) based on their
# membership in other sets.  In this case there are 3 prinicpals. Mary and Bob
# are encoding what fruits they are willing to eat in the oset Bob.what2eat and
# Mary.what2eat.  Ralphs is defining the prices of the fruits it sells by
# assigning them to osets parameterized by price.
#
# Credential 1 lays out Mary's policy - she will eat fruits that cost less that
# or equal to 2.00.  Bob will eat fruits that cost between 1.00 and 5.00, as
# laid out in Credential 2.  Strictly speaking Bob and Mary's rules apply to
# prices assigned by Ralphs.  Credentials 3-6 are Ralph's price assignments:
# 
# Fruit               Price      Credential
# apple               1.50       3
# kiwi                1.50       4
# black rasberry      2.50       5
# navel ornage        0.50       6
#
# The query.py file proves that Mary will eat a naval orange and a kiwi,
# and that Bob will not eat a navel orange (too cheap).  It also proves a apple
# is priced at 1.50 and that it cannot prove that a green apple is priced at
# 1.50 (we don't know anything about green apples).

# fruits_rt2

# Credential 1
# [keyid:mary].oset:what2eat 
#      <- [keyid:ralphs].oset:fruitprice([float:?P:[..2.00]])

# Credential 2
# [keyid:bob].oset:what2eat 
#      <- [keyid:ralphs].oset:fruitprice([float:?P:[1.00..5.00]])

# Credential 3
# [keyid:ralphs].oset:fruitprice([float:1.50]) <- [string:'apple']

# Credential 4
# [keyid:ralphs].oset:fruitprice([float:1.50]) <- [string:'kiwi']

# Credential 5
# [keyid:ralphs].oset:fruitprice([float:2.50]) <- [string:'black rasberry']

# Credential 6
# [keyid:ralphs].oset:fruitprice([float:0.50]) <- [string:'navel orange']