This document discusses properties of task environments in artificial intelligence. It begins with learning outcomes which are to differentiate task environments based on properties and identify those properties. It then lists and describes several key properties of task environments: fully observable vs partially observable, single agent vs multiagent, deterministic vs stochastic, episodic vs sequential, static vs dynamic, discrete vs continuous, and known vs unknown. Examples are provided for each property, such as taxi driving being stochastic and non-deterministic. A table compares example environments based on these properties. The document concludes with references for further reading.

1. Mr. Vipul H. Kondekar
(vhkondekar@witsolapur.org)
Assistant Professor,
Electronics & Telecommunication Engineering
Walchand Institute of Technology, Solapur
(www.witsolapur.org)
Artificial Intelligence :
Properties of task environments
Walchand Institute of Technology, Solapur 1

2. At the end of this session, students will be able to
1) Differentiate task environments based on different properties.
2) Identify properties of task environments.
Walchand Institute of Technology, Solapur 2
Learning Outcomes

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Contents:
• Fully observable vs. Partially observable
• Single agent VS. multiagent
• Deterministic vs. stochastic
• Episodic vs. sequential
• Static vs. dynamic
• Discrete vs. continuous
• Known vs. unknown
Properties of task environments

4. Agent and Environment
Environment
Agent
percepts
actions
?
Sensors
Effectors
Walchand Institute of Technology, Solapur

5. Walchand Institute of Technology, Solapur 5
Task environments
Automated self driving Car
Environment
A taxi must deal with a variety of roads
Traffic lights, other vehicles, pedestrians, stray
animals, road works, police cars, etc.

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Properties of task environments
• Fully observable vs. Partially observable
• If an agent’s sensors give it access to the complete state of the environment at
each point in time then the environment is fully observable
• An environment might be Partially observable because of noisy and inaccurate
sensors or because parts of the state are simply missing from the sensor data.
• Fully observable environments are convinient because the agent need not manitain any internal
state to keep track of the world.

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Properties of task environments
• Single agent VS. multiagent
– Playing a crossword puzzle – single agent
– Chess playing – two agents
– Competitive multiagent environment
• Chess playing
– Cooperative multiagent environment
• Automated taxi driver
• Avoiding collision

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Properties of task environments
• Deterministic vs. stochastic
– Next state of the environment Completely determined by the current state and the
actions executed by the agent, then the environment is deterministic, otherwise, it
is Stochastic.
– Environment is uncertain if it is not fully observable or not deterministic
– Outcomes are quantified in terms of probability
-taxi driver is Stochastic
- Vacuum cleaner may be deterministic or stochastic

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Properties of task environments
• Episodic vs. sequential
– An episode = agent’s single pair of perception & action
– The quality of the agent’s action does not depend on other episodes
• Every episode is independent of each other
– Episodic environment is simpler
• The agent does not need to think ahead
• Sequential
– Current action may affect all future decisions
-Ex. Taxi driving and chess.

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Properties of task environments
• Static vs. dynamic
– A dynamic environment is always changing over time
• E.g., the number of people in the street
– While static environment
• E.g., the destination
• Semidynamic
– environment is not changed over time
– but the agent’s performance score does
– E.g., chess when played with a clock

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Properties of task environments
• Discrete vs. continuous
– If there are a limited number of distinct states, clearly defined percepts
and actions, the environment is discrete
– E.g., Chess game, Taxi driving

12. Characteristics of environments
Fully
observable?
Deterministic? Episodic? Static? Discrete? Single
agent?
Solitaire
Backgammon
Taxi driving
Internet
shopping
Medical
diagnosis
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Think ?

13. Characteristics of environments
Fully
observable?
Deterministic Episodic Static Discrete? Single
agent?
Solitaire No Yes Yes Yes Yes Yes
Backgammon
Taxi driving
Internet
shopping
Medical
diagnosis
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14. Characteristics of environments
Fully
observable?
Deterministic? Episodic? Static? Discrete? Single
agent?
Solitaire No Yes Yes Yes Yes Yes
Backgammon Yes No No Yes Yes No
Taxi driving
Internet
shopping
Medical
diagnosis
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15. Characteristics of environments
Fully
observable?
Deterministic? Episodic? Static? Discrete? Single
agent?
Solitaire No Yes Yes Yes Yes Yes
Backgammon Yes No No Yes Yes No
Taxi driving No No No No No No
Internet
shopping
Medical
diagnosis
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16. Characteristics of environments
Fully
observable?
Deterministic? Episodic? Static? Discrete? Single
agent?
Solitaire No Yes Yes Yes Yes Yes
Backgammon Yes No No Yes Yes No
Taxi driving No No No No No No
Internet
shopping
No No No No Yes No
Medical
diagnosis
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Characteristics of environments
Fully
observable?
Deterministic? Episodic? Static? Discrete? Single
agent?
Solitaire No Yes Yes Yes Yes Yes
Backgammon Yes No No Yes Yes No
Taxi driving No No No No No No
Internet
shopping
No No No No Yes No
Medical
diagnosis
No No No No No Yes

18. References
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• Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig,
3rd Edition, Prentice Hall
• A First Course in Artificial Intelligence, Deepak Khemani, McGraw Hill
Education (India)
• Introduction to Artificial Intelligence & Expert Systems, Dan W Patterson,
PHI.
• Artificial Intelligence, Elaine Rich and Kevin Knight, Tata McGraw Hill

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Thanks!!