Environment Types In Artificial Intelligence Examples – Understanding the AI environment is a very difficult task but there are several key dimensions that shed light on the matter.
Each artificial intelligence (AI) problem is a new world of unique difficulties and challenges. Often, the most challenging aspects of solving an AI problem are not finding the solution but understanding the problem itself. As surprising as that sounds, even the most experienced AI experts have been guilty of rushing to recommend deep learning algorithms and exotic optimization techniques without fully understanding the problem at hand. When we think about an AI problem, we tend to link our reasoning to two main elements: datasets and models. However, that argument ignores what could be considered the most challenging aspect of the AI problem: the environment.
Environment Types In Artificial Intelligence Examples
When we build artificial intelligence (AI) solutions, we spend a lot of time focusing on aspects such as learning algorithm design [eg: supervised, unsupervised, semi-supervised], neural network architecture [eg: influence, recurrent… ] or properties of data [eg: labeled, unlabeled…]. However, little attention is often given to the nature of the environment in which AI solutions operate. As it turns out, environmental characteristics are the number one factor that can make or break an AI model.
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There are several features that distinguish the AI landscape. The shape and frequency of the data, the nature of the problem, the amount of knowledge available at any given time are some of the factors that distinguish one type of AI environment from another. A deep dive into those characteristics will guide the strategies of AI professionals in areas such as algorithm selection, neural network architecture, optimization techniques and many other important aspects of the AI software lifecycle. Understanding the AI environment is a very difficult task but there are several key dimensions that shed light on the matter.
One of the best ways to understand the AI environment is to categorize it into a series of well-known dimensions that are often divided into two or three classifications. Among the different characteristics that can be used to characterize the AI landscape, there are seven key unique trends that provide a quick understanding of the challenges and capabilities needed by AI agents.
One of the most obvious dimensions for classifying an AI environment depends on the number of agents involved. The majority of AI models today focus on single-agent environments but there is an increasing expansion into multi-agent settings. The introduction of multiple agents in an AI problem raises challenges such as cooperative or competitive dynamics that do not exist in a single-agent environment.
A perfect AI environment is one in which, at any given moment, the agents have enough information to complete a branch of the problem. Chess is a classic example of a full AI environment. Poker, on the other hand, is an imperfect environment as AI strategies can only anticipate many moves in advance and, instead, focus on finding the perfect ‘equilibrium’ at any given moment. Many of the popular Nash equilibrium principles are very useful in the imperfect AI environment.
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A fully visible AI environment can access all the information needed to complete a target task. Image recognition works in fully visible domains. Virtual environments such as those encountered in self-driving cars involve virtual information to solve AI problems. Quantitative environments often rely on statistical methods to increase knowledge of the environment.
Competitive AI environments pit AI agents against each other to improve specific outcomes. Games like GO or Chess are examples of competitive AI environments. Collaborative AI environments rely on collaboration between multiple AI agents. Cars that drive themselves or cooperate to avoid collisions or interactions with smart home sensors are examples of collaborative AI environments. Many multi-agent environments such as video games include cooperative and competitive dynamics that make them particularly challenging from an AI perspective.
Static AI environments rely on data knowledge sources that do not change frequently over time. Speech analysis is a problem that works on a static AI environment. Compared to that model, dynamic AI environments such as AI vision systems on drones deal with constantly changing data sources. A dynamic AI environment often needs to enable rapid and regular training of AI agents.
A specific AI environment is one in which a set of possibilities [albeit arbitrarily large] can drive the final outcome of a task. Chess is also classified as a unique AI problem. A sustainable AI environment relies on unknown and rapidly changing data sources. Massively multiplayer video games are a classic example of a sustainable AI environment.
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A deterministic AI environment is one in which outcomes can be determined based on specific circumstances. By decision, we specifically refer to an AI environment that ignores uncertainty. Most real-world AI environments are non-deterministic. Instead, they can be classified as stochastic. Self-driving cars are one of the most advanced examples of typical AI environments but simpler settings can be found in simulation environments or even speech analysis models.
Understanding the AI environment is one of the most challenging steps in any AI problem. Fortunately, the friction points in the seven dimensions explored in this article often provide a robust classification of AI environments and facilitate the selection of models and architectures. While there have been notable advances in AI architecture and optimization techniques, environmental analysis remains an advanced aspect of the AI lifecycle.
Biography: Jesus Rodriguez is a technology expert, venture capitalist and startup consultant. A software scientist by background, Jesus is an internationally recognized speaker and author with contributions that include hundreds of articles and presentations at industry conferences.2 Question 1 Which of the following is true about the ‘Rational Agent’? A rational agent chooses an action that maximizes its measure of performance based on a sequence of perception and built-in knowledge A rational agent chooses a sequence of perception that maximizes its measure of performance based on action and built-in knowledge A rational agent chooses an action with a. a sequence that maximizes its measure of performance given built-in knowledge A rational agent chooses an action that maximizes its measure of performance based on the perceptual sequence alone.
3 Question 1 Which of the following is true about a ‘Rational agent’? A rational agent chooses an action that maximizes its measure of performance based on a sequence of perception and built-in knowledge A rational agent chooses a sequence of perception that maximizes its measure of performance based on action and built-in knowledge A rational agent chooses an action with a. the sequence that maximizes its performance measure based on built-in knowledge A rational agent chooses the action that maximizes its performance measure based on the perception sequence Only answer (a)
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Which of the following is the correct match from the table above? A-I, B-II, C-III, D-IV A-III, B-IV, C-I, D-II A-IV, B-III, C-I, D-II A-III, B-IV, C-II , D Role of Agent A) Goal Based Agent i) Uses state-action rules B) Utility Agent ii) Stores conceptual history representation C) Simple Reflex Agent iii) Uses search and planning D) Reflex Agent of Example iv) Produces superior behavior
Which of the following is the correct match from the table above? A-I, B-II, C-III, D-IV A-III, B-IV, C-I, D-II A-IV, B-III, C-I, D-II A-III, B-IV, C-II , D Answer(b) Role of Agent A) Goal Based Agent i) Uses state-action rules B) Service Oriented Agent ii) Stores conceptual history representation C) Simple Reflex Agent iii) Uses search and planning D) Agent Model Reflex iv) Produces superior behavior
Question 3 Opinion Type of Agent Actions Objectives Environment Architecture System of clinical examination Symptoms, results, patient responses Questions, tests, treatment Healthy patient, cost reduction Patient, hospital ? Satellite image analysis system Different scale sizes, colors Print classification of scene Accurate classification of images from satellite orbiting part picking robot Pixels of different intensity Take parts and sort them into bins Place parts into correct bins Conveyor belt and parts Fuel filter regulator Temperature, pressure. lessons Open, close valves; adjust temperature Increase purity, yield, safety Cleaner Interaction English teacher Written words Print exercises, suggestions, corrections Add student’s marks on the test Set of students Figure 2.3 Examples of types of agents and their descriptions PAGE. (Artificial Intelligence A Modern Approach by Stuart Russell and Peter Norvig. 1st Edition, Prentice Hall, 1995) Question : For each environment in Figure 2.3, determine which type of agent architecture is most appropriate (table view, simple reflex, objective – basis or basis of use).
Question 3 Opinion Type of Agent Actions Objectives Environment Design System of clinical examination Symptoms, results, patient’s response Questions, tests, treatment Healthy patient, reduce costs Patient, hospital Satellite image analysis system Different sizes, color Print scene classification Correct classification of images from orbiting satellite Part picking robot Pixels of different sizes Pick up parts and sort them into bins Place parts into correct bins Conveyor belt with parts Purifier regulator, pressure gauges Open, close valves ; adjust temperature Increase purity, yield, safety Cleaner Interaction English teacher Written words Print exercises, suggestions, corrections Add student’s marks on the test Set of students Figure 2.3 Examples of types of agents and their descriptions PAGE. (Artificial Intelligence A Modern Approach by Stuart Russell and Peter Norvig. 1st Edition, Prentice Hall, 1995)
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