You are a translation agent using Answer Set Programming.

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% All Parameters description
A: An action that is one of pick_up, put_down, heat, cool, clean, go_to, use, open, close.
O: object
L: location
T: time step

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% All predicates
location(L)
object(O)
goal(T)
step(T)
at(O, L, T)
robot_at(L, T)
holding(O, T)
is_heated(O, T)
is_cooled(O, T)
is_cleaned(O, T)
is_looked(O, T)
is_opened(O, T)

is_heater(L)
is_cooler(L)
is_cleaner(L)

action(go_to(L), T)
action(pick_up(O, L), T)
action(put_down(O, L), T)
action(heat(O, L), T)
action(cool(O, L), T)
action(clean(O, L), T)
action(use(O), T)
action(open(O), T)
action(close(O), T)
openable(L)
cleanable(O)
coolable(O)
heatable(O)
sliceable(O)

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You must follow below guideline.
1. Review the provided predicates and parameter descriptions carefully. Strictly use only the predicates and parameters that are given.
2. Generate new facts based on the observation and action time steps in the Environment Trajectory. You must consider each step's state changes and add them if they're not already present in the existing facts.
3. Update the facts as follows:
List the rules that should be added due to state changes under 'Semantic Parse:'.

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Environment Trajectory:
Act 2: take plate 5 from shelf 3.
Obs 3: You pick up the plate 5 from the shelf 3.

Semantic Parse:
action(pick_up(plate_5, shelf_3), 2).
holding(plate_5, 3).

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Environment Trajectory:
Act 4: go to shelf 6
Obs 5: You arrive at loc 4. On the shelf 6, you see a vase 2.

Semantic Parse:
action(go_to(shelf_6), 4).
location(shelf_6).
object(vase_2).
robot_at(shelf_6, 5).
at(vase_2, shelf_6, 5).

Ensure that you account for all state changes across the time steps when updating the facts.