OMP9016-Assignment 1 Solved

The purpose of this assignment is to assess the following LearningOutcomes:

•   LO1 Appraise domain specific formalisms used in knowledge representation schemes.

•   LO2 Compare and contrast current knowledge representation approaches integrated in systems relevant to AI.

•   LO3 Select, apply and evaluate a knowledge representation scheme for a specified domain.

1.1 BUILDING YOUR WORLD
Create a 2D world that will play host to a game to be played by your agent types. Provide context for the mechanics of the world and the conditions under which the game is complete. Theintentofthisexerciseisforyoutodemonstrateyourunderstandingofhowaproblemcan be modeled abstractly. To this end, provide a well a defined task environment, implement three different agent types and provide a PEAS description for each.

•   Critique the advantages and disadvantages of each agent type.

•   Demonstrate each agents ability to perform or under-perform in the 2-dimensional world.

•   Discuss, and evaluate, the agents suitability to operate in world of varying sizes.

The solution should be implemented in Python, you may use any of the libraries made available from the AIMA python repository but they must be clearly referenced (see note on importing code under submission), you will be graded on your contribution and this should be clearly highlighted.

Write a clear and concise description of the agent-based world. The purpose of this is to articulate an understanding of the underlying concepts being implemented both from a theoretical and practical perspective.

1.2 SEARCHING YOUR WORLD
•   Formulate a well defined problem statement and identify a goal-state under which your game is complete. Why is this important to search? As part of your solution you should be including the initial state, the set of actions, the transition model, a goal test function and a path cost function.

•   Select three uninformed search techniques and discuss their appropriateness to your world under appropriate headings for evaluating problem-solving performance. Implement the uninformedsearch techniques and discuss the results.

•   Select three informed search techniques and discuss their appropriateness to your world under appropriate headings for evaluating problem-solving performance. Implement the informedsearch techniques and discuss the results.

Write a clear and concise report detailing the search techniques performance in your agentbased game for the relevant agent types. The purpose of this is to articulate an understanding of the underlying concepts, and limitations, being implemented both from a theoretical and practical perspective.

1.3 FORWARD-CHAINING AND BACKWARD-CHAINING
Forward-Chaining and Backward-Chaining introduces the capacity for inference in an environment. How does this benefit the operation of an agent, in particularly in your world? Provide a short critical analysis of both approaches. Thereafter demonstrate their applicability by utilising them in your world.

2 TIPS
•   Tip for Part 1: Review “agents.ipynb” and Chapter 2 of the recommended text. Do not overthinkthis, startfastandfailfaster, defineyourtaskenvironment, outlineyourPEAS for the three agent tpyes. You can start with basic environments first and iteratively increase the complexity of the mechanics of the world - just be sure to do so.

•   TipforPart2: Review “search.ipynb” and Chapter 3 and 4 of the recommended text.

•   Tip for Part 3: Forward and backward-chaining are instances of AND-OR search trees, read up on them on wikipedia, review Chapters 4, 7 and 8 from the recommended text particularly (Section 4.3.2). Review “AND-OR Graph Search” in “search.ipynb” and backward/forward chaining in “logic.ipynb”.

•   TipforallParts: Do not provide masses of text lifted from the text/online or wikipedia. Be your own editor, focus on what is important and articulate that in a rationale way that is technically accurate. Demonstrating a capacity to explain complex topics in a clear and concise manner is indicative of a higher level of understanding and will be rewarded with an appropriate grade. Use results derived from experimental runs to back your claims and make sure your narrative is coherent and consistent.