AI search strategies using discrete mathematics

In summary, to create a new composite heuristic h3 that is admissible and dominates h1 and h2, the formula (h1+h2)/2 can be used. However, this may not always result in the most optimal solution. The cost of each state according to h3 would be lower than h1, but not necessarily lower than h2.
  • #1
rcmango
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Homework Statement



The question is to provide a new composite heuristic h3 that is admissible and dominates h1 and h2. Then show the cost of each of the states through H according to h3.

Homework Equations



States:
A B C D E F G H
h1: 10,12,12,6,7,7,2,10
h2: 8, 9, 14,4,9,7,1,9


The Attempt at a Solution


h1, h2 : {A, B, C, D, E, F, G, H} -> {1, 2, ...}.
Both are admissible,
for example: h1(x) <= C(x) and h2(x) <= C(x) for all x, where C(x) is the lowest cost to the goal.
using h3(x) = (h1(x) + h2(x)) / 2, then h3(x) will be admissible, this may not be better than h1 or h2.

I've tried using (h1+h2)/2 = h3

So, using 2 elements from h1 and h2, h3(A) = (10 + 8) / 2 = 9 < 10 = h1(A)

so the new path cost is lower than h1, but not h2, so is that necessarily more dominate? Not sure what formula would create an optimal solution.
 
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  • #2
The cost of each of the states according to h3 would be: A: 9, B: 10.5, C: 13, D: 5, E: 8, F: 7, G: 1.5, H: 9.5
 

FAQ: AI search strategies using discrete mathematics

What is the role of discrete mathematics in AI search strategies?

Discrete mathematics provides the theoretical foundation for many AI search algorithms. It helps in analyzing the efficiency and complexity of algorithms, as well as in designing and implementing them. Discrete mathematics concepts such as graphs, trees, and combinatorics are used to model and solve AI search problems.

What are some common AI search strategies that use discrete mathematics?

Some common AI search strategies that use discrete mathematics include depth-first search, breadth-first search, A* search, and heuristic search. These strategies use various discrete mathematics concepts and algorithms to search through large search spaces and find optimal solutions.

How does discrete mathematics help in optimizing AI search algorithms?

Discrete mathematics helps in optimizing AI search algorithms by providing tools and techniques to analyze the efficiency and complexity of the algorithms. This allows for the development of more efficient and effective search strategies, which can save time and resources in finding solutions to complex problems.

What are some challenges in using discrete mathematics for AI search strategies?

One of the main challenges in using discrete mathematics for AI search strategies is the ability to accurately model the problem using discrete structures. This requires a deep understanding of both the problem domain and the discrete mathematics concepts being used. Additionally, the size and complexity of the search space can also present challenges in finding optimal solutions.

How is discrete mathematics used in machine learning and artificial intelligence?

Discrete mathematics is used in machine learning and artificial intelligence to model and solve complex problems, such as pattern recognition, natural language processing, and decision making. It provides the necessary tools and techniques to analyze and optimize algorithms, which are crucial in building intelligent systems that can learn and make decisions on their own.

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