1. An adaptive mobile robot system with knowledge-driven architecture in communications with subject matter experts that describe the situation and assign robot tasks with situational requirements and comprising: Distributed specialized knowledge nodes that store adaptive behavior models related to specific knowledge domains and provide components of these models as situational service scenarios with rule-based service invocations, while participating in transformation of new situational requirements into situational service scenarios; at least one robot team with at least one robot that includes: a) A plurality of robot sensors that produce sensor data; b) A plurality of robot controllers that via service invocations define robot's behavior; c) Service Orchestration Engine connected to robot controllers and transforming service orchestration scenarios into rule-based service invocations, which can be executed by robot controllers; d) Specialized knowledge node (SKN) that interacts internally with Service Orchestration Engine within the robot and externally with distributed specialized knowledge nodes and subject matter experts to transform incoming situational requirements, information from other specialized knowledge nodes and robot sensor data into service orchestration scenarios that define robot's behavior models, wherein SKN includes: (i) Service Dictionary describes and stores adaptive behavior models for each robot as a dynamically changeable set of services, service orchestrations that consist of composite services with a plurality of rule-based service invocations, and situational service scenarios that consist of plurality of composite services assembled into groups connected by conditional rules, which include sensor data describing situational components, where each group represents a component of robot's behavior in a specific situation; (ii) Natural Language Interpreter interprets natural language based information coming from subject matter experts into a map of knowledge topics, subjects related to a described situation called in the future conversational subjects; (iii) Situational Scenario Interpreter working in collaboration with the Natural Language Interpreter to transform-natural language based situational description and changeable situational task requirements into situational service scenarios that represent adaptive behavior models executed by robots; (iv) Specialized Data Interpreter interprets plurality of special data formats including XML-based service descriptions, mechanical and video sensor data and transforms them into components of service orchestration scenarios; (v) Conversation Manager initiates and maintains collaborative conversations between robots, specialized knowledge nodes and subject matter experts, while requesting existing components of situational service scenarios from distributed specialized knowledge nodes and additional information, corrections and clarifications of situational requirements from subject matter experts while transforming situational requirements into behavior models defined with situational service scenarios; (vi) Knowledge Bus Manager distinguishes different types of data incoming to the common bus from internal and external sources, uses publish-subscribe mechanism to publish this information in message queues providing sequence and type identification to each data source, and makes this data available to distributed knowledge nodes and subject matter experts as well as internally to components-subscribers within SKN including Situational Scenario Interpreter, Natural Language Interpreter, Special Data Interpreter, Service Orchestration Engine, Service Dictionary and Conversational Manager.