Conversational Agent Training via Natural Language Mapping
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Solution Overview
Problem
Conversational agents are limited by pre-defined formal knowledge representation, making it difficult to apply them to new problem domains without redefining their knowledge representation, and they struggle to understand natural language inputs that do not map to existing scripts.
Innovation Solution
A conversational agent is trained through natural language input using a set of atomic operators and a rules-based artificial intelligence engine, which allows users to map natural language inputs onto scripts and generalize understanding, enabling the system to expand its capabilities through user interaction and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-defined formal knowledge representation is used, then the conversational agent can execute existing scripts, but it cannot apply to new problem domains without redefining knowledge representation
Solution Approach 1:
The system enables users to train the conversational agent directly through natural language interactions. The agent learns new domain-specific language mappings and scripts through user-provided examples, eliminating the need for developers to manually redefine knowledge representations for each new domain. The user serves as the trainer, providing training data that automatically expands the agent's capabilities.
Solution Approach 2:
The knowledge representation evolves dynamically from a static pre-defined set of scripts to a dynamic system that grows through user training. The mapping rules and scripts are not fixed but can be continuously expanded and adapted based on user interactions and training data, allowing the system to adapt to new domains without complete redefinition.
2Reliability
If pre-defined scripts are used, then the system can process known inputs, but it struggles to understand natural language inputs that do not map to existing scripts
Solution Approach 1:
The system implements a feedback mechanism where users can provide training data when the conversational agent encounters unfamiliar natural language inputs. Instead of simply failing to understand new inputs, the system allows users to teach the agent new language patterns and mappings, which are then stored and used for future similar inputs. This feedback loop continuously improves the agent's understanding while maintaining reliable script execution for known patterns.
3Stability of the object's composition
If manual redefinition of knowledge representation is required for new domains, then existing scripts can be maintained, but the development time and effort increase significantly
Solution Approach 1:
The system performs preliminary learning by allowing users to provide training examples in advance. Rather than requiring manual redefinition when a new domain is needed, the system can be pre-trained with user-provided examples of domain-specific language and concepts. This preliminary action of user training prepares the system ahead of time, eliminating the need for time-consuming manual redefinition later.
Data Source
AI summary
Aspects of the present disclosure relate to training a conversational agent. A conversational agent is initiated with a set of atomic operators, each atomic operator having one or more atomic operator rules for executing operations. A natural language input is received from a user on a conversational interface. A determination is made whether the natural language input maps to an existing script by referencing a rules engine. In response to a determination that the natural language does not map to an existing script, training is requested from the user. Training is then received from the user which maps the natural language input to a script. A mapping rule which maps the natural language input to the script is stored, and the script is executed.