Indirect and Conditional Sensing in the Event Calculus

Controlling the sensing of an environment by an agent has been accepted as necessary for effective operation within most practical domains. Usually, however, agents operate in partially observable domains where not all parameters of interest are accessible to direct sensing. In such circumstances, sensing actions must be chosen for what they will reveal indirectly, through an axiomatized model of the domain causal structure, including ramifications. This article shows how sensing can be chosen so as to acquire and use indirectly obtained information to meet goals not otherwise possible. Classical logic Event Calculus is extended with both a knowledge formalism and causal ramifications, and is used to show how inferring unknown information about a domain leads to conditional sensing actions.

• Type: Conference
• Year: 2004
• Where: ECAI
• Authors: Jeremy Forth, Murray Shanahan
• Keywords: Artificial Intelligence, Conditional Sensing Actions, ECAI 2004, Sensing, Sensing Actions