This is a AI based search program. The agent is located in a 120 * 160 grid consisting of highways, hard to traverse terrain and blocked cells. Agents needs to travel from start to end node using different search methods.

An agent operating in a grid-world has to quickly compute a path from the current cell it occupies to a target goal cell. Different cells in the grid have different costs for traversing them. For instance, some may correspond to impassable obstacles, others to flat and easy to traverse regions. There may also be passable but difficult to traverse regions (e.g., rocky, granular terrain or swamps) as well as regions that can accelerate the motion of a character (e.g., highways or navigable regions, etc.).

We will consider grid maps of dimension 160 columns and 120 rows with square cells that can be blocked, unblocked, partially blocked and directions in the grid where the cost can be decreased.

• Green cell: Start Vertex
• Red cell: End Vertex
• White cells: Unblocked
• Light gray cells: Partially blocked
• Dark gray cells: Blocked
• Blue line indicates a direction of motion along which the motion of the agent can be accelerated
• Yellow line indicates a path taken by the agent

A sample map is shown below:

Cells are selected on a predefined conditions which you can read from attached assignment description file.

Following graph traversal algorithms have been implemented:

1. Uniform Cost Search
2. A*
3. Weighted A*
4. Sequential Heuristic A*
5. Integrated Heuristic A*

To implement these algorithm we created our own version of binary heap instead of using standard libraries. We call this special data structure Fringe. It is a priority queue which supports the following operations:

• insert(element, queue): add element and return the resulting queue
• remove(queue): remove and return the first element in the queue
• empty(queue): return true if no elements
• initialize(element): create a queue that contains element

Uniform Cost Search (UCS) calculates the path cost of all the nodes in the fringe and expands the one that has the minimum cost.

Figure above describes the operation of the algorithm. After the initial node S is expanded, three nodes are generated: A, B and C. Among them node A has the smallest path cost and is selected for expansion, generating node G, a goal node. Then node B is the node with the minimum path cost and is expanded first, also resulting in a goal node. The goal node produced by B is the optimal solution and the node that will be visited first by UCS.

The A* search algorithm is defined as:

f(n) = g(n) h(n)

The evaluation function is the sum of the true path from the initial node to node n and the heuristic value at n. In this way A* selects nodes that optimize the estimate for the complete path cost.

A* maintains two global data structures:

1. First, the fringe (or open list) is a priority queue that contains the vertices that A∗ considers to expand. A vertex that is or was in the fringe is called generated. The fringe provides the following procedures:
   • Procedure fringe.Insert(s, x) inserts vertex s with key x into the priority queue fringe.
   • Procedure fringe.Remove(s) removes vertex s from the priority queue fringe.
   • Procedure fringe.Pop() removes a vertex with the smallest key from priority queue fringe and returns it.
2. Second, the closed list is a set that contains the vertices that A* has expanded and ensures that A* expands every vertex at most once.

A* uses a user-provided constant h-value (= heuristic value) h(s) for every vertex s ∈ S to focus the search, which is an estimate of the distance to the goal, i.e., an estimate of the distance from vertex s to the goal vertex. A* uses the h-value to calculate an ƒ-value ƒ (s) = g(s) h(s) for every vertex s, which is an estimate of the distance from the start vertex via vertex s to the goal vertex.

Weighted A* expands the states in the order of ƒ = g w · h values, where w ≥ 1. In the case that w = 1, Weighted A* is identical to the original A* algorithm. When w > 1, the search is biased towards vertices that are closer to the goal according to the heuristic.

This approach aims to utilize information from many different heuristics, which are considered by running n 1 searches in a rather sequential manner. Each search uses its own, separate priority queue. Therefore, in addition to the different h values, each state uses a different g value for each search. We use g0 to denote the g for an anchor search process, which must use an admissible/consistent heuristic for the overall process to return an optimal solution. We use g_i, (i = 1, 2, ..., n) for the other search procedures, which can make use of any type of heuristic including inadmissible ones.

This algorithm takes the above idea one step further. In particular, the current path for a given state is shared among all the search procedures, i.e., if a better path to a state is discovered by any of the search processes, the information is updated in all priority queues. As the paths are shared, this Integrated Heuristic A* uses a single g value for each state, unlike the previous process in which every search maintains its own g value. Furthermore, this path integration allows the algorithm to expand each state at most twice, in contrast to the Sequential approach, which may expand a state up to n 1 times (once in each search). Yet, the Integrated Heuristic A* can achieve the same bounds as the Sequential one.

Path from start to end node is calculated and displayed on the map. Other things displayed are:

• Run time
• Path Cost
• Path length
• Total number of nodes expanded
• Total memory requirement