Artificial Intelligence Agents by Wolfgang Ertel

Although the term intelligent agents is not new to AI, only in recent years has it gained prominence. Agent denotes rather generally a system that processes information and produces an output from an input. These agents may be classified in many different ways.

Fig. 1.5 A software agent with user interaction

In classical computer science, software agents are primarily employed. In this case the agent consists of a program that calculates a result from user input.

Fig. 1.6 A hardware agent

In robotics, on the other hand, hardware agents (also called autonomous robots) are employed, which additionally have sensors and actuators at their disposal The agent can perceive its environment with the sensors. With the actuators it carries out actions and changes its environment.

With respect to the intelligence of the agent, there is a distinction between reflex agents, which only react to input, and agents with memory, which can also include the past in their decisions. For example, a driving robot that through its sensors knows its exact position (and the time) has no way, as a reflex agent, of determining its velocity. If, however, it saves the position, at short, discrete time steps, it can thus easily calculate its average velocity in the previous time interval.

If a reflex agent is controlled by a deterministic program, it represents a function of the set of all inputs to the set of all outputs. An agent with memory, on the other hand, is in general not a function. Why? Reflex agents are sufficient in cases where the problem to be solved involves a Markov decision process. This is a process in which only the current state is needed to determine the optimal next action.

A mobile robot which should move from room 112 to room 179 in a building takes actions different from those of a robot that should move to room 105. In other words, the actions depend on the goal. Such agents are called goal based.

Example 1.1 A spam filter is an agent that puts incoming emails into wanted or unwanted (spam) categories, and deletes any unwanted emails. Its goal as a goal-based agent is to put all emails in the right category. In the course of this not-so-simple task, the agent can occasionally make mistakes. Because its goal is to classify all emails correctly, it will attempt to make as few errors as possible. However, that is not always what the user has in mind. Let us compare the following two agents. Out of 1,000 emails, Agent 1 makes only 12 errors. Agent 2 on the other hand makes 38 errors with the same 1,000 emails. Is it therefore worse than Agent 1? The errors of both agents are shown in more detail in the following table, the so-called "confusion matrix":

Agent 1 in fact makes fewer errors than Agent 2, but those few errors are severe because the user loses 11 potentially important emails. Because there are in this case two types of errors of differing severity, each error should be weighted with the appropriate cost factor.

The sum of all weighted errors gives the total cost caused by erroneous decisions. The goal of a cost-based agent is to minimize the cost of erroneous decisions in the long term, that is, on average. The medical diagnosis system LEXMED as an example of a cost-based agent.

Analogously, the goal of a utility-based agent is to maximize the utility derived from correct decisions in the long term, that is, on average. The sum of all decisions weighted by their respective utility factors gives the total utility.

Of particular interest in AI are Learning agents, which are capable of changing themselves given training examples or through positive or negative feedback, such that the average utility of their actions grows over time.

Distributed agents are increasingly coming into use, whose intelligence are not localized in one agent, but rather can only be seen through cooperation of many agents.

The design of an agent is oriented, along with its objective, strongly toward its environment, or alternately its picture of the environment, which strongly depends on it sensors. The environment is observable if the agent always knows the complete state of the world. Otherwise the environment is only partially observable. If an action always leads to the same result, then the environment is deterministic. Otherwise it is nondeterministic. In a discrete environment only finitely many states and actions occur, whereas a continuous environment boasts infinitely many states or actions.

About the Author

Dr. Wolfgang Ertel is a professor at the Institute for Artificial Intelligence at the Ravensburg-Weingarten University of Applied Sciences, Germany.

This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning.

Topics and features:

Presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website
Contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons
Includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning
Reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art
Examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayes theorem and its relevance in everyday life
Discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search
Includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation

Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.

A reader says,"The book has a substantial introduction to logic that was very helpful. Overall the selection of topics was perfect for a first course in AI."

A reader says,"An excellent book for beginners. Would like to have seen code examples in python. I realize python is not much historic for AI but it is lingua franca for data science and deep learning today. Good treatment of many topics on search etc. Definitely a great resource for those just getting started in AI."

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