「現場で活用するためのAIエージェント実践入門」でつまずいたことメモ:2章 - Qiita

はじめに 「現場で活用するためのAIエージェント実践入門」の第2章で私がつまずいたことのメモです。 （このメモのほかの章へ：1章 / 2章） 第2章 AIエージェントの構成 この章ではAIエージェントの構成を学びます。 2.1 AIエージェントの内部構成 AIエ...

Behavior adaptation for mobile robots via semantic map compositions of constraint-based controllers

Specifying and solving Constraint-based Optimization Problems (COP) has become a mainstream technology for advanced motion control of mobile robots. COP programming still requires expert knowledge to transform specific application context into the right configuration of the COP parameters (i.e., objective functions and constraints). The research contribution of this paper is a methodology to couple the context knowledge of application developers to the robot knowledge of control engineers, which, to our knowledge, has not yet been carried out. The former is offered a selected set of symbolic descriptions of the robots’ capabilities (its so-called “behavior semantics”) that are translated in control actions via “templates” in a “semantic map”; the latter contains the parameters that cover contextual dependencies in an application and robot vendor-independent way. The translation from semantics to control templates takes place in an “interaction layer” that contains 1) generic knowledge about robot motion capabilities (e.g., depending on the kinematic type of the robots), 2) spatial queries to extract relevant COP parameters from a semantic map (e.g., what is the impact of entering different types of “collision areas”), and 3) generic application knowledge (e.g., how the robots’ behavior is impacted by priorities, emergency, safety, and prudence). This particular design of, and interplay between, the application, interaction, and control layers provides a structured, concept...

ICAPS 2003 Workshop on PDDL

ICAPS 2003 Workshop on PDDL

Effective grounding for hybrid planning problems represented in PDDL+ | The Knowledge Engineering Review | Cambridge Core

Effective grounding for hybrid planning problems represented in PDDL+ - Volume 36