Considerations for making agents behave intelligently

How to make agents behave intelligently

In this article, we consider how to make agents behave intelligently, as described in ‘Artificial life and agent technology’.

1. Designing intelligent behaviour: To implement intelligent behaviour in an agent, it is first necessary to define the following basic elements

• Goal: the goal to be achieved by the agent.
• Environment: modelling the conditions and constraints of the world in which the agent operates.
• Decision-making: the ability to select the most appropriate action to achieve the goal.

2. basic approaches:

(1) Rule-based systems.
– Features: decides on actions based on predefined rules, e.g. the rule base described in ‘Rule Base, Knowledge Base, Expert Systems and Relational Data’.
– Example: IF-THEN rules describe the behaviour of an agent.
– Advantages: simple to implement, easy to control.
– Disadvantages: cannot flexibly cope with complex environments or unexpected situations.

(2) Learning agents based on machine learning
– Features: learns patterns based on past data and experience and responds flexibly to new situations.
– Example: learns behaviours that maximise rewards from the environment using reinforcement learning as described in ‘Theories and algorithms of various reinforcement learning techniques and their implementation in python’.
– Advantages: adaptable to diverse environments, evolves with experience.
– Disadvantages: requires training data and computational resources.

(3) Multi-agent systems
– Features: multiple agents mutually cooperate or compete with each other to solve problems, as described in e.g. ‘Overview and implementation examples of multi-agent systems using graph neural networks’.
– Examples: intelligent behaviour in team AI and competitive games.
– Advantages: decentralised decision-making, can handle large problems.
– Disadvantages: coordination between agents can be difficult.

3. implementation of intelligent elements

(1) Knowledge representation: formalising and describing information in order for an agent to understand a situation.
– Methods: knowledge graphs as described in ‘Knowledge information processing techniques’ and the Semantic Web as described in ‘Semantic Web technologies’.
– Examples of applications: object recognition and conversation understanding.

(2) Reasoning engines: logical reasoning based on existing knowledge, as described in ‘Reasoning technology’.
– Methods: logical inference (inference engines) and Bayesian inference as described in ‘Bayesian inference model building and inference – overview and models of hidden Markov models’.
– Applications: prediction, diagnosis, decision-making.

(3) Reinforcement learning: learning behaviour through interaction with the environment.
– Methods: Q-learning as described in ‘Overview of Q-learning, algorithms and implementation examples’, Deep Q-Networks (DQN) as described in ‘Overview of Deep Q-Networks (DQN), algorithms and implementation examples’, Policy Gradient as described in ‘Overview of Policy Gradient methods, algorithms and implementation examples’.
– Applications: robot control, game AI.

(4) Natural language processing (NLP): the ability to understand and generate human language.
– Methods: the transformer models described in ‘Overview of Transformer Models, Algorithms and Example Implementations’ (GPT described in ‘Overview of GPT, Algorithms and Example Implementations’, BERT described in ‘Overview of BERT, Algorithms and Example Implementations’).
– Applications: conversational agents, translation, document summarisation.

(5) Computer vision: processing visual information and recognising surroundings.
– Methods: convolutional neural networks (CNNs) as described in ‘Overview of CNNs, algorithms and implementation examples’, object detection models (YOLO as described in ‘Overview of YOLO (You Only Look Once), algorithms and implementation examples’, Faster R-CNN as described in ‘Overview, algorithms and implementation examples of Faster R-CNN’).
– Applications: automatic driving, face recognition.

4. application examples

(1) Autonomous vehicles
– Intelligent behaviour: recognises surrounding vehicles and pedestrians and selects safe routes.
– Methods: computer vision, path planning algorithms (A*, Dijkstra).

(2) Virtual assistant
– Intelligent behaviour: answering questions, scheduling.
– Methods: NLP, dialogue management with reinforcement learning.

(3) Smart factory
– Intelligent behaviour: optimises production lines, detects anomalies.
– Methods: predictive maintenance with machine learning, IoT sensor data analysis.

5. how to sustainably enhance intelligent behaviour

• Learning from the environment: adapting behaviour based on new situations and data.
• Use of simulation: learn optimal behaviour through repeated trial and error in a virtual environment.
• Human-in-the-loop: improve decision-making through interaction with humans.

Technology selection and design are important to give agents intelligent behaviour, and by combining the right approaches according to the environment and purpose.

Solutions that make agents behave intelligently

The following are specific applications and the challenges and value that can be solved in each of these areas.

1. customer service

• Solution examples
  – Chatbots: respond to customer queries 24 hours a day and resolve issues quickly.
  – Personalised support: make appropriate suggestions based on customer history and preferences.
  – Omni-channel support: unify multiple platforms for efficient support.
• Value
  – Increased customer satisfaction.
  – Reduced labour costs.
  – Reduced time to problem resolution.

2. smart manufacturing (Industry 4.0)

• Solution examples
  – Automatic quality inspection: automatic detection of product defects with computer vision.
  – Predictive maintenance: minimise production line downtime by predicting machine faults in advance.
  – Optimised production planning: adjust production according to demand and supply in real time.
• Value.
  – Increased production efficiency.
  – Cost savings.
  – Reduced waste.

3. healthcare

• Solution examples
  – Diagnostic support system: analyses medical data to support early diagnosis of diseases.
  – Rehabilitation robots: optimised rehabilitation plans based on the patient’s movements.
  – Telemedicine assistant: connects patients and doctors and shares data in real time.
• Value
  – Improved quality of healthcare.
  – Efficient use of healthcare resources.
  – Remote accessible healthcare services.

4. autonomous mobility systems

• Solution examples.
  – Self-driving cars: recognise their surroundings and select safe routes.
  – Drone delivery: efficiently deliver packages to their destinations.
  – Robotic cleaning: efficient cleaning of homes and commercial premises.
• Value
  – Improved mobility and logistics efficiency.
  – Improved safety.
  – Reduced human workload.

5. education and training

• Solution examples.
  – Intelligent tutoring: individual learning plans based on student progress and understanding.
  – VR training system: allows students to experience hazardous tasks and special situations in a virtual environment and learn skills safely.
  – AI-assisted material generation: optimises learning materials to the learning style of the individual.
• Value
  – Increased learning efficiency.
  – Ensures safety in high-risk environments.
  – Personalisation of education.

6. entertainment

• Solution examples.
  – NPCs (non-player characters): add dynamic and realistic character behaviour to games and simulations.
  – Interactive stories: generate stories that unfold according to the user’s choices.
  – Live avatars: virtual characters that mimic voice and facial expressions in real time.
• Value.
  – Increased immersion.
  – Enhanced user involvement.
  – Provides a personalised experience.

7. environmental management

• Solution examples.
  – Smart agriculture: utilises soil sensors and weather data to deliver water and fertiliser at the optimum time to maximise yield.
  – Energy optimisation: analysing energy consumption in buildings to reduce waste.
  – Environmental monitoring: use drones and sensors to monitor forests and water quality.
• Value.
  – Sustainable environmental management.
  – Efficient use of resources.
  – Contribution to environmental protection.

8. security

• Solution examples.
  – Fraud detection: analyses financial transaction data to predict fraud.
  – Cyber security assistant: detects network vulnerabilities in real time.
  – Surveillance systems: AI cameras detect abnormal behaviour and send out alerts.
• Value.
  – Increased safety.
  – Risk reduction.
  – Faster problem response.

9. personalised marketing

• Solution examples.
  – Recommendation engines: suggest products and services based on personal preferences.
  – Chat-based purchasing assistant: motivates users to buy through user interaction.
  – Dynamic pricing described in”Machine learning and algorithms used for surge pricing and examples of implementation“: adjusts prices based on demand and individual customer profiles.
• Value
  – Increased customer engagement.
  – Maximisation of sales.
  – Efficient implementation of marketing strategies.

Giving agents intelligent behaviour can solve a range of challenges, such as increasing efficiency, personalisation and safety. These solutions also increase convenience for society as a whole, as well as creating new business models.

implementation example

Examples of implementations with intelligent behaviour in agents are described.

1. chatbot

Example implementation: agent generating answers to questions

import openai

# Set the OpenAI API key.
openai.api_key = 'YOUR_API_KEY'

def chatbot_response(user_input):
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a knowledgeable agent."},
            {"role": "user", "content": user_input}
        ]
    )
    return response['choices'][0]['message']['content']

# Test user input.
user_input = "Tell us about how AI works."
response = chatbot_response(user_input)
print("AI agent response.:", response)

build a simple chatbot that responds to questions using Python and OpenAI’s GPT model

2. autonomous mobile agents

Example implementation: simulation of a robot solving a maze

Use Python and the reinforcement learning library GYM to learn how the agent solves the maze.

import gym
import numpy as np

# Create environment.
env = gym.make("Taxi-v3")

# Q table initialisation
q_table = np.zeros([env.observation_space.n, env.action_space.n])
alpha = 0.1  # learning rate
gamma = 0.6  # discount rate
epsilon = 0.1  # Probability of search

# Q Learning algorithm
for i in range(1000):
    state = env.reset()
    done = False

    while not done:
        if np.random.uniform(0, 1) < epsilon:
            action = env.action_space.sample()  # random selection
        else:
            action = np.argmax(q_table[state])  # Actions based on Q-tables.
        
        next_state, reward, done, _ = env.step(action)
        old_value = q_table[state, action]
        next_max = np.max(q_table[next_state])
        
        # Q-value update
        q_table[state, action] = (1 - alpha) * old_value + alpha * (reward + gamma * next_max)
        
        state = next_state

# Check learning results.
print("Learned Q table:n", q_table)

3. simulation of automated vehicles

Implementation example: autonomous vehicle following the road

Simple autonomous driving is implemented using the Carla simulation environment.

import carla

client = carla.Client('localhost', 2000)
client.set_timeout(10.0)
world = client.get_world()

# Spawn vehicle.
blueprint_library = world.get_blueprint_library()
vehicle_bp = blueprint_library.filter('model3')[0]
spawn_point = world.get_map().get_spawn_points()[0]
vehicle = world.spawn_actor(vehicle_bp, spawn_point)

# Automatic operation control
control = carla.VehicleControl()
control.throttle = 0.5  # progress
vehicle.apply_control(control)

4. education sector

Example implementation: an AI tutor to track learning progress.

Agents that analyse student answer data and identify weak points.

import numpy as np

# Learning data (e.g. percentage of correct answers for each question)
student_data = {
    "math": [1, 0, 1, 1, 0],
    "science": [1, 1, 0, 0, 1],
    "history": [0, 0, 1, 1, 1]
}

# Identification of weak points.
def analyze_weakness(data):
    weakness = {subject: np.mean(scores) for subject, scores in data.items()}
    return sorted(weakness.items(), key=lambda x: x[1])

weaknesses = analyze_weakness(student_data)
print("Weak subjects.:", weaknesses)

5. environmental monitoring

Example implementation: forest monitoring using drones

Use Python and the drone control library dronekit to monitor a specified area.

from dronekit import connect, VehicleMode, LocationGlobalRelative
import time

# Connected to the drone
vehicle = connect('127.0.0.1:14550', wait_ready=True)

# takeoff function
def arm_and_takeoff(target_altitude):
    while not vehicle.is_armable:
        print("Drone in preparation....")
        time.sleep(1)
    
    vehicle.mode = VehicleMode("GUIDED")
    vehicle.armed = True
    
    while not vehicle.armed:
        print("inside the arm...")
        time.sleep(1)
    
    print("Take-off!")
    vehicle.simple_takeoff(target_altitude)
    
    while True:
        print("hight: ", vehicle.location.global_relative_frame.alt)
        if vehicle.location.global_relative_frame.alt >= target_altitude * 0.95:
            print("Target altitude reached")
            break
        time.sleep(1)

# execute
arm_and_takeoff(10)  # Take-off to an altitude of 10 m
vehicle.mode = VehicleMode("LAND")
vehicle.close()

6. entertainment sector

Example implementation: game character AI (pathfinding)

In Python and Pygame, a character aims for the goal while avoiding obstacles.

import pygame
import heapq

def a_star_search(grid, start, goal):
    open_set = []
    heapq.heappush(open_set, (0, start))
    came_from = {}
    cost_so_far = {start: 0}
    
    while open_set:
        _, current = heapq.heappop(open_set)
        
        if current == goal:
            break
        
        for neighbor in get_neighbors(grid, current):
            new_cost = cost_so_far[current] + 1  # Travel costs
            if neighbor not in cost_so_far or new_cost < cost_so_far[neighbor]:
                cost_so_far[neighbor] = new_cost
                priority = new_cost + heuristic(goal, neighbor)
                heapq.heappush(open_set, (priority, neighbor))
                came_from[neighbor] = current
    return reconstruct_path(came_from, start, goal)

reference book

Reference books for giving agents intelligent behaviour are described below.

1. basic concepts and theory
– Artificial Intelligence: A Modern Approach (AI:AMA)
Authors: Stuart Russell, Peter Norvig
This world-renowned AI textbook provides a detailed explanation of the fundamentals of intelligent agent design and application.

– Multi-Agent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Authors: Yoav Shoham, Kevin Leyton-Brown
A good book on the theory and practice of multi-agent systems. Recommended, especially if you are interested in cooperation and competition between agents.

– Reinforcement Learning: An Introduction
Authors: Richard S. Sutton, Andrew G. Barto
Learn how agents adapt to their environment through reinforcement learning. 2.

2. implementation and algorithms
– Programming Collective Intelligence.
Author: Toby Segaran
The book is rich in examples of practical AI and data processing implementations using Python.
The implementation of recommendation systems and pathfinding are particularly informative.

– Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Author: Aurélien Géron
A practical guide to implementing machine learning in Python. Learn how to enhance the intelligent behaviour of agents with machine learning.

– Deep Reinforcement Learning Hands-On
Author: Maxim Lapan
Explains how to implement learning agents, with a focus on reinforcement learning.

3. multi-agents and social behaviour
– An Introduction to MultiAgent Systems.
Author: Michael Wooldridge
An introduction to multi-agent systems. The mechanisms of cooperation, competition and negotiation are carefully explained.

– Game Theory for Applied Economists
Author: Robert Gibbons
An introduction to game theory for understanding decision-making processes and strategic behaviour among agents.

– Designing Sociable Robots
Author: Cynthia Breazeal
Ideal if you are interested in human-agent interaction. Useful for designing socially intelligent agents.

4. practical applications
– AI and Games.
Authors: Georgios N. Yannakakis, Julian Togelius
Focuses on AI in games, detailing how agents interact with players.

– Autonomous Agents and Multi-Agent Systems.
Author(s): Gita Sukthankar et al.
Presents an approach to designing autonomous agents and solving real-world problems.

– Learning from Demonstration (Springer Series in Advanced Robotics).
Authors: Sonia Chernova, Andrea L. Thomaz
Reference book on designing agents that learn and imitate human behaviour.

5. areas of specialisation
– Robotics-related
– Robot Operating System (ROS) for Absolute Beginners
Author: Lentin Joseph
An introduction to ROS for learning how to run simple agents on robots.

– Natural language processing
– Speech and Language Processing
Authors: Daniel Jurafsky, James H. Martin
Learn how to build natural language processing capabilities into agents.

– Financial sector.
– Algorithmic Trading and DMA
Author: Barry Johnson
Focuses on algorithmic trading and strategy design using agents.