Intelligent Agents Flashcards
agent
anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
percept
the content an agent’s sensors are perceiving
percept sequence
the complete history of everything that agent has ever perceived
agent function
maps percept sequences to actions
agent program
the internal method the agent has of generating results for the agent function
rational agent
chooses actions which maximize its expected performance measure
task environment
the “problems” to which rational agents are the “solutions”
PEAS
Performance
Environment
Actuators
Sensors
PEAS - Performance measure
What is the agent trying to achieve? How will we measure it?
PEAS - Environment
Where the agent exists?
PEAS - Actuators
How can the agent change the environment?
PEAS - Sensors
What can the agent observe from its environment?
fully observable vs. partially observable
Are the agents sensors efficient enough to see everything that it needs to efficiently max its performance measure?
single agent vs. multi-agent
does it need to model and acknowledge other agents or is there only one?
deterministic vs. non-deterministic
deterministic - if you know that past you know the future
non-deterministic - contain events where events occur no one knows the outcome of
stochastic vs non-deterministic
stochastic - explicity deals with probabilities (25% chance of rain tomorrow)
non-deterministic - possibilities are listed without being quantified (chance of rain tomorrow)
episodic vs. sequential
episodic - agent’s experience is divided into atomic episodes, in each episode agent receives a percept and then performs a single action (crucially, the next episode does NOT depend on the actions taken in previous episodes)
sequential - current decision could affect all future decisions
static vs. dynamic
dynamic if the environment can change while an agent is deliberating
static otherwise
known vs unknown
known - the outcomes for all actions are given
unknown - the agent will have to learn how it works in order to make good decisions
