Task Environment In Artificial Intelligence – 3 Agents “An agent is anything that can be viewed as perceiving its environment through sensors and acting on that environment through stimuli” “The agent’s choice of action at any given instant can depend on the entire sequence of perceptions observed so far” Perceptual sequence complete. History of everything that agents have ever known Vimal EA C461- Artificial Intelligence
4 Agent The behavior of the agent is described by the function of the agent, the mapping of any perceptive sequence to execute the agent’s internal action program of the agent of the artificial agent Vimal EA C461- Artificial Intelligence.
Task Environment In Artificial Intelligence
Implementation of recognition sequence [A, clean] right [A, dirty] suction [B, clean] left [B, dirty] [A, clean], [A, clean] [A, clean], [A, dirty] … [ A, Clean], [A, Clean], [A, Clean] [A, Clean], [A, Clean], [A, Dirty] Percepts: location and contents, e.g., [A, Dirty] Actions: left, right, Suck, NoOp Simple Agent Function Vimal EA C461- Artificial Intelligence
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Agree with the reason; reasonable; sensible: a strategic plan for economic development. Having or using reason, good judgment, or good sense: calm and reasonable deliberation equation. in or characterized by the full possession of one reason; Consciousness; lucid: The patient appears perfectly rational. endowed with the faculty of reason: all reasonable. Of, pertaining to, or constituting the rational faculty: the rational faculty. Action or from reason or based on reason: logical explanation. … Vimal EA C461- Artificial Intelligence
A rational agent is one who does the right thing. The right action is the one that makes the agent more successful. Appropriate action measures for the vacuum world. Is reasonable at any point in time? Vimal EA C461- Artificial intelligence
8 Reasonable agent For each possible sequence of awareness, the rational agent should choose the action expected to maximize its measurement, given the evidence provided by the sequence of awareness and any knowledge generated, the agent has. Are our vacuum cleaner agents reasonable? Vimal EA C461- Artificial Intelligence
Actual Optimization Vs. Maximizing Expected Performance Learning autonomy A rational agent is autonomous learning to compensate for partial/inaccurate knowledge Vimal EA C461- Artificial Intelligence
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10 Task Environment Task environment is the “problem” that the rational agent is the “solution” includes performance measures Environment Actuator Sensors Vimal EA C461- Artificial Intelligence
Sensors Taxi Driver safe, fast, legal, comfort, maximum profit on the road, other traffic, pedestrians, customer guidance, acceleration, brake, signal, horn Camera, sonar, GPS, Speedometer, keyboard, etc. Medical diagnosis system, patient health, reduce costs, patient lawsuits, hospitals, employees Screen display (questions, Testing, Diagnosis, Treatment, Referral) Keyboard (Symptom Entry, Findings, Patient Response) PEAS Description Vimal EA C461- Artificial Intelligence
Fully Observable (vs. Partly Observable) Agent sensors provide the complete environment at each point in time Sensors detect all aspects involved in choosing an action Deterministic (vs. Stochastic) The next state of the environment is completely determined by the current state and actions taken by agents of the strategic environment Vimal EA C461- Artificial Intelligence
Episodic (vs. Sequential) The agent’s experience can be divided into episodes, each episode with what the agent perceives and what the next episode does not depend on the previous episode Decisions made now will affect all future sates in a sequential environment Static (vs. Dynamic ) The environment does not change because the agent is deliberating Semi dynamic Vimal EA C461- Artificial Intelligence
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Discrete (vs. Continuous) depends on how time is handled in describing the situation. Image, Percept, Performance Chess Game: Taxi Driving Separately: Continuous Single-Agent (vs. Multi-Agent) Competition, Multi-Agent Cooperation Communication is a key issue in multi-agent. Environment Vimal EA C461- Artificial Intelligence
Fully Observable Deterministic Episodic Static Discrete Single agent Examples of Task Environments and Their Classrooms Vimal EA C461- Artificial Intelligence
Perform the function of the agent, perform the mapping of the percepts to the operation of the computer device running the agent program, with sensors and actuators Vimal EA C461- Artificial Intelligence
To make this website work, we record user data and share it with processors. To use this website, you must agree to our privacy policy, including our cookie policy. Understanding the AI environment is an incredibly complex task but there are several important dimensions that provide clarity on that rationale.
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Every artificial intelligence (AI) problem is a new universe of complexities and unique challenges. Often, the most challenging aspect of solving an AI problem is not about finding a solution but understanding the problem itself. Conversely, even the most experienced AI experts are guilty of rushing to propose deep learning algorithms and exoteric optimization techniques without fully understanding the problem at hand. When we think about AI problems, we tend to associate our reasoning with two main aspects: datasets and models. However, that reasoning ignores what can be considered the most challenging aspect of the AI problem: the environment.
When designing artificial intelligence (AI) solutions, we spend a lot of time focusing on aspects such as the structure of the learning algorithm [such as supervised, unsupervised, semi-supervised], the architecture of the neural network [such as convolutional, recurrent … ] or the characteristics of the data [such as labeled, unlabeled …]. However, little attention is often paid to the nature of the environment in which AI solutions operate. As it turns out, environmental characteristics are the number one element that can make or break an AI model.
There are many aspects that distinguish the AI environment. The shape and frequency of data, the nature of the problem, the amount of knowledge available at any given time are some of the elements that separate one type of AI environment from another. Digging deeper into those characteristics will guide AI experts’ strategies in areas such as algorithm selection, neural network architecture, optimization techniques and many related aspects of the AI application life cycle. Understanding the AI environment is an incredibly complex task, but there are several important dimensions that provide clarity on that rationale.
One of the most effective methods for understanding the AI environment is to categorize it along well-known dimensions that are often divided into two or three classifications. Among the different characteristics that can be used to classify the AI environment, there are seven important special policies that provide a quick understanding of the challenges and capabilities needed by AI agents.
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One of the most obvious dimensions in classification and AI environments is based on the number of agents involved. Most AI models today focus on single-agent environments but are growing in multi-agent settings. Bringing multiple agents into AI problems poses challenges such as cooperation or competitive dynamics that are not present in a single-agent environment.
Complete AI environments are those where, at any given time, the agent has enough information to complete a branch of the problem. Chess is a classic example of a complete AI environment. On the other hand, Poker is an imperfect environment because AI strategies can only predict many moves in advance and, instead, they focus on finding a good “equilibrium” at any given time. The most famous Nash equilibrium principle is particularly relevant in incomplete AI environments.
A fully observable AI environment has access to all the necessary information to complete the target task. Image recognition operates in a fully observable domain. Partially observable environments, such as events encountered in self-driving cars, deal with partial data to solve AI problems. Some observable environments often rely on statistical techniques to expand knowledge about the environment.
A competitive AI environment pits AI agents against each other to optimize specific outcomes. Games like GO or Chess are examples of competitive AI environments. Cooperative AI environments are based on cooperation between multiple AI agents. Self-driving vehicles or cooperative collision avoidance or smart home sensor interactions are examples of collaborative AI environments. Multi-agent environments such as video games include both cooperative and competitive aspects, which makes them particularly challenging from an AI perspective.
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A static AI environment relies on knowledge sources that don’t change over time. Speech analysis is a problem that runs on static AI environments. In contrast to that model, dynamic AI environments such as AI vision systems in drones deal with constantly changing data sources. Dynamic AI environments often require faster and more regular training of AI agents.
A unique AI environment is one that determines the possibility [albeit arbitrarily large] that it can drive the final outcome of the task. Chess is also classified as a separate AI problem. Continuous AI environments rely on unknown and rapidly changing data sources. Multiplayer video games are classic examples of continuous AI environments.
The AI environment defines what results can be determined based on a specific state. By definition, we specifically mean an AI environment that ignores uncertainty. Most real-world AI environments are undefined. Instead, they can be classified as stochastic. Self-driving vehicles are the most extreme example of a stochastic AI environment but simple settings can be found in simulation environments or even speech analysis.
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