[SOLVED] AI Java algorithm Microsoft Word G53DIA Coursework 1 Description.docx

Microsoft Word G53DIA Coursework 1 Description.docx

CourseworkDescription

Thecourseworkinvolvesthespecification,designandimplementationofa
simple agent.

Coursework Requirements

The problem consists of a 2D environment, in which a single agent must collect and
disposeofwaste(CO2)from stations,e.g.,carboncaptureandstorage.Stations
periodically generate tasks requests to dispose of a specified amount of waste. The
environment also contains a number of wells where waste can be deposited. The goal
of the agent is to dispose of as much waste as possible in a fixed period of time.

Task Environment

The standard task environment is defined as:

the environment is an infinite 2D grid that contains randomly distributed

stations, wells and refuelling points
stations periodically generate tasks requests to dispose of a specified amount

of waste
tasks persist until they are achieved (a station has at most one task at any time)
the maximum amount of waste that must be disposed of in a single taskis

1,000 litres
wells can accept an infinite amount of waste
refuelling points contain an infinite amount of fuel
in each run, there is always a refuelling station in the centre of the

environment
a run lasts 10,000 timesteps
an agent can sense only its current position (which may be a station, well or

refuelling point)
the agent can take waste from a station and dispose of it in a well
moving around the environment requires fuel, which the agent must replenish

at a fuel station
the agent can carry a maximum of 100 litres of fuel and 1,000 litres of waste
the agent starts out in the centre of the environment (at the fuel station) with

100 litres of fuel and no waste
the agent moves at 1 cell / timestep and consumes 1 litre of fuel / cell
filling the fuel and waste tanks and delivering waste to a well takes one

timestep
if the agent runs out of fuel, it can do nothing for the rest of the run
the success (score) of an agent in the task environment is determined by the

amount of waste delivered

Thetaskenvironmentshouldnotbemodifiedorextended.Allotherdecisions
regarding software design and implementation strategy are up to you.

Youmustimplementanagentthatcompletesthetaskinthespecifiedtask

Environment.

Task environment in detail 1
the environment is discrete and consists of a grid of cells
the environment contains randomly distributed stations, wells and refuelling points
stations periodically generate tasks requests to dispose of a specified amount of
waste (max 5,000 litres)
tasks persist until they are achieved (a station has at most one task at any time)
wells contain can accept an infinite amount of waste
refuelling points contain an infinite amount of fuel

Task environment in detail 2

the agent can see any stations, wells and refuelling points within 25 cells of its current position
if a station is visible, the agent can see if it has a task, and if so, how much waste is to
be disposed of
the agent can carry a maximum of 100 litres of fuel and 1,000 litres of waste
the agent moves at 1 cell / timestep and consumes 1 litre of fuel / cell
filling the fuel and waste tanks and disposing of waste in a well takes one timestep
if the agent runs out of fuel, it can do nothing for the rest of the run

Task environment in detail 3

in each run, there is always a refuelling point in the centre of the environment
the agent starts out in the centre of the environment (at the fuel station) with 100 litres
of fuel and no waste
a run lasts 10,000 timesteps
the success (score) of the agent in the task environment is determined by the total amount
of waste delivered

Resources

AJavademoagentpackageisprovidedasastarting pointforyourprojectwork.This
provides animplementationofthestandardtask environment and a very basic agent that
chooses actions at random.

Java agent packageas a starting point for your project work
implementation of the task environment which generates a random set of stations, wells
and refuelling points for each run, and periodically generates tasks
an abstract agent class which provides methods for sensing and acting
a concrete demo agent, that chooses actions at random
all you have to do is (re)write the action selection function

Evaluation

state the performance of your agent, i.e., what score does it achieve (on average)
explain why your solution is (or is not) appropriate for the task environment, e.g.:

explain which features of the task environment are critical for your solution to work well
explain how would you expect your agent to perform in different task environments

once you have made the high-level decisions, think about how each aspect of the
agent could/should be implemented
e.g., how will the agent search the environment, or decide what what to do next
will your agent always use the same action selection function, or will the action
selection function vary with time, etc.
you can use algorithms from agent case studies in the lectures, from previous AI courses
or AI textbooks, or invent your own solution

Efficient exploitation

which task to do next arbitrary choice (first one, random choice, etc,..)?
evaluate alternatives (closest, largest amount of waste, etc?)
how to collect waste for a task
opportunistically, or when required?
which makes best use of time/fuel?
when a new task is discovered
should the agent do it now
add it to the list of tasks?
re-evaluate which task to do next?
which task is best?

Choosing tours
we can think of a trip which completes one or more tasks as a tour
we can then reformulate the problem as which tour is best?
where should the tour begin/end?
in which order should the agent visit stations/wells/fuel pump?
how long should the tour be?

Improving a tour
one way to plan is to start with a single-task tour and ask: can this be improved?
what do we mean by improved?
one possible definition: a tour can be improved if the agent can get a
better outcome for a little extra effort
what do we mean by better outcome?
what do we mean by effort?

how much is a little?
can we quantify the improvement?