From b0a96a989f907d11f5cdc39085e1b984d799c548 Mon Sep 17 00:00:00 2001
From: Atharva Raut <115960812+athar-va@users.noreply.github.com>
Date: Tue, 8 Aug 2023 14:04:42 -0400
Subject: [PATCH] Update README.md

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-## 16:198:520 : Project 2 : Circle of Life
-The purpose of this project is to build a probabilistic model of an environment in the presence of uncertainty, and
-use it to inform and direct decision making. You are pursuing a prey object, while simultaneously being pursued
-by a predator object, and want to capture your target and evade capture yourself. This will be complicated by not
-necessarily being able to see where the predator and prey are - but you must use the information you have to make
-the most informed decisions you can about what to do.
-
-Concepts used:
-Breadth First Search
-Markov Decision Process
-Conditional Probability Models
+# Purpose of the Project
+
+Here, we help an agent catch a prey in the presence of an adversory (predator) in different information settings listed below. The rules of movement of prey and predator are set. We build a probablistic model based on these rules and using Markov Decision Process , we attempt to catch the prey.
+
+# Building the Environment
+
+- The environment is a graph of 50 interconnected nodes in a circular arrangement.
+- Agent, prey, and predator move between these nodes.
+- Edges are added randomly for greater connectivity:
+  - Random nodes with degree less than 3 are chosen.
+  - Edges between these nodes and others within 5 steps forward or backward are added.
+  - This process continues until no more edges can be introduced.
+
+# Entities' Behaviors:
+
+- The Prey: Movement involves random selection among neighbors or the current cell, with equal probabilities.
+- The Predator: Available neighbors are assessed for shortest distance to the Agent. Movement occurs to the nearest neighbor. In case of tie, random selection is made.
+- The Agent: Movement follows a specific strategy. In situations with limited information, surveying a distant node for its content is an option. The Agent possesses awareness of the decisions of the Predator and Prey, although specific actions remain unknown.
+
+# Strategy Descriptions
+
+- Agent 1: Precise knowledge of Predator and Prey locations is possessed by the Agent.During its turn, available neighbors are examined and selected based on these criteria:
+  * Closer to the Prey and farther from the Predator.
+  * Closer to the Prey but not closer to the Predator.
+  * Not farther from the Prey and farther from the Predator.
+  * Not farther from the Prey but not closer to the Predator.
+  * Farther from the Predator.
+  * Not closer to the Predator.
+  * Remaining stationary.
+- Agent 2: Beats Agent 1
+- Agent 3: Knows the location of predator but not the prey.
+- Agent 4: Beats Agent 3
+- Agent 5: Knows the location of the prey but not the predator.
+- Agent 6: Beats Agent 5
+- Agent 7: Does not know locations of both the entities. 
+
+# Technologies and Libraries Used
+
+- Python
+
+# Concepts Used
+
+- Graph theory
+- Game theory
+- Markov Decision Process
+
+#GraphTheory #GameTheory #BeliefStates #PythonProject#NoisySurveys #Drones #PythonCoding #ArtificialIntelligence #MDP