Intelligent Agents

Intelligent Agents: An Intelligent Agent must sense, must act, must be autonomous (to some extent),. It also must be rational. AI is about building rational agents. An agent is something that perceives and acts. A rational agent always does the right thing.

• What are the functionalities (goals)?

• What are the components?

• How do we build them?

Rationality: Perfect Rationality assumes that the rational agent knows all and will take the action that maximizes her utility. Human beings do not satisfy this definition of rationality. Rational Action is the action that maximizes the expected value of the performance measure given the percept sequence to date. However, a rational agent is not omniscient. It does not know the actual outcome of its actions, and it may not know certain aspects of its environment. Therefore rationality must take into account the limitations of the agent. The agent has too select the best action to the best of its knowledge depending on its percept sequence, its background knowledge and its feasible actions. An agent also has to deal with the expected outcome of the actions where the action effects are not deterministic.

Bounded Rationality: “Because of the limitations of the human mind, humans must use approximate methods to handle many tasks.” Herbert Simon, 1972

Evolution did not give rise to optimal agents, but to agents which are in some senses locally optimal at best. In 1957, Simon proposed the notion of Bounded Rationality: that property of an agent that behaves in a manner that is nearly optimal with respect to its goals as its resources will allow. Under these promises an intelligent agent will be expected to act optimally to the best of its abilities and its resource constraints.

Agent Environment: Environments in which agents operate can be defined in different ways. It is helpful to view the following definitions as referring to the way the environment appears from the point of view of the agent itself.

Observability: In terms of observability, an environment can be characterized as fully observable or partially observable. In a fully observable environment all of the environment relevant to the action being considered is observable. In such environments, the agent does not need to keep track of the changes in the environment. A chess playing system is an example of a system that operates in a fully observable environment. In a partially observable environment, the relevant features of the environment are only partially observable. A bridge playing program is an example of a system operating in a partially observable environment.

Determinism: In deterministic environments, the next state of the environment is completely described by the current state and the agent’s action. Image analysis systems are examples of this kind of situation. The processed image is determined completely by the current image and the processing operations. If an element of interference or uncertainty occurs then the environment is stochastic. Note that a deterministic yet partially observable environment will appear to be stochastic to the agent. Examples of this are the automatic vehicles that navigate a terrain, say, the Mars rovers robot. The new environment in which the vehicle is in is stochastic in nature. If the environment state is wholly determined by the preceding state and the actions of multiple agents, then the environment is said to be strategic. Example: Chess. There are two agents, the players and the next state of the board is strategically determined by the players’ actions.

Episodicity: An episodic environment means that subsequent episodes do not depend on what actions occurred in previous episodes. In a sequential environment, the agent engages in a series of connected episodes.

Dynamism: Static Environment: does not change from one state to the next while the agent is considering its course of action. The only changes to the environment are those caused by the agent itself.

  • A static environment does not change while the agent is thinking.
  • The passage of time as an agent deliberates is irrelevant.
  • The agent doesn’t need to observe the world during deliberation.

A Dynamic Environment changes over time independent of the actions of the agent — and thus if an agent does not respond in a timely manner, this counts as a choice to do nothing

Continuity: If the number of distinct percepts and actions is limited, the environment is discrete, otherwise it is continuous.

Presence of Other agents

Single agent/ Multi-agent: A multi-agent environment has other agents. If the environment contains other intelligent agents, the agent needs to be concerned about strategic, game-theoretic aspects of the environment (for either cooperative or competitive agents) Most engineering environments do not have multi-agent properties, whereas most social and economic systems get their complexity from the interactions of (more or less) rational agents.

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