Rational Agents
In a rational agent a goal can be specified by some performance metric.
A rational agent will choose the the actions that maximizes the expected value.
Inputs to an agent
- Abilities A set of actions that the agent can perform
- Goal What is the agent trying to achieve
- Prior Knowledge What sis the agent kno when it came into being
- History
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- Stimuli: The current environment
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- Past experiences
Agent System
An agent systems is an agent in an environment
receives **stimuli** and caries out **actions**
Dimensions of an Agent
An agent can be defined in many ways or dimensions that specifies its complexity and structure.
Modularity
How is the agent structured
- Flat Agent has one level.
- Modules Agent has many interlinking modules.
- Hierarchy Agent has a hierarchy(recursive) of modules.
Planning horizon
How far ahead is the agent expected to plan
- Static World does not change.
- Finite Stages The agent plans a finite number of steps.
- Indefinite The agent plans a finite but unknown number of steps.
- Infinite The agent plans continuously.
Representation
How is the environment represented
- Explicit states A state is one of the ways the world can be.
- Features or Propositions States can be described using features.
- Individuals and relations Feature of relationships between sets of individuals.
Computational limits
How is the agent limited by computation
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Perfect rationality The agent can determine the best course of action.
- Bounded rationality The agent must make a good decision with computation and memory limitations.
Learning from experience
does the agent learn form experience
- Knowledge is given at creation.
- Knowledge is learnt from past experiences.
Sensing uncertainty
How well does the agent know its environment
- Fully observable The agent knows its entire environment.
- Partially observable There is a limited number of states, the observation is nto true to reality.
Effect uncertainty
Cant the result of an effect be known
- Deterministic The resulting state can be known form the previous state and action.
- Stochastic There us uncertainty about the environment after the action.
Preference
What is the agents desire
- Achievement goal The agent must reach a goal which could be the result of a complex formulae.
- Complex preferences The agent has a range of complex preferences that interact with one another.
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- Ordinal Only the order of the preferences matters.
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- Cardinal The value of the preferences matters.
Number of Agents
- Single Agent All other agents are part of the environment.
- Multiple Agent Agents will reason about the actions of other.agents
Interaction
How does the agent interact with the environment
- Reason offline reason before taking action.
- Reason online reason while taking action.
Representation
To compute a problem the problem must be in a computable representation before an output can be provided
Requirements
An representation should be
- Rich enough to express the knowledge needed
- As close to the problem as possible
- Amenable to efficient computation
- Able to be acquired
Solution
Quality of a solution
- Optimal Solution The best solution possible
- Satisfactory solution A solution that is good enough
- Approximate solution A solution that is close to an optimal solution
- Probable solution A solution that is likely to be correct
Decisions and Outcomes
Good decisions can have bad outcomes and Bad decisions can have good outcomes
Information can lead to better decisions.
Computation time can provide a better solution.
An Any time algorithm an produce a solution at any time but given more computation time the solution gets better
A worse solution now maybe be better than an optimal solution later.
Physical symbol Hypothesis
A symbol is a physical pattern that can be represented.
A symbol-system has the means to manipulate symbols.
hypothesis a Physical symbol system has the means for general intelligent action
Knowledge and symbol levels
- Knowledge level : Agent’s knowledge, beliefs and goals.
- Symbol level Describes what reasoning agent does. This is what the agent uses to implement the knowledge level
Abstraction
- Low level Easier for machines to understand
- High level Easier for humans to understand
Reasoning and acting
- Design time reasoning and computation Reasoning done by designer to design the agent.
- Offline computation Done by agent before it has to act
- Online computation Done by agent “on the go”
Knowledge base and observations
- Knowledge base Compiled background knowledge and data
- Observations Information obtained online
- Both used to make decisions