Adaptive Dialog Agent Using Sub-Agents for User Preference Alignment

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Solution Overview

Problem

Existing dialog agents lack the ability to adaptively generate responses that align with user-defined preferences for dialog features such as emotion, objective-relatedness, confusion, personality, and verbosity, leading to suboptimal user interactions.

Innovation Solution

An adaptive user-facing dialog agent that generates multiple candidate responses and selects the most appropriate one based on predetermined global dialog objectives, utilizing a set of sub-agents each trained on distinct machine learning models tailored to specific dialog features.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a dialog agent uses multiple sub-agents with distinct machine learning models to generate candidate responses tailored to different dialog features, then the adaptability and alignment with user preferences improve, but the device complexity increases

Engineering Contradiction:

Improveadaptability to user preferencesVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The dialog agent is segmented into multiple specialized sub-agents, each responsible for specific dialog features (emotion, confusion, personality, verbosity). Each sub-agent has its own machine learning model trained on particular aspects of dialog generation, allowing the system to handle different features independently and efficiently.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The dialog agent system performs multiple functions by integrating several sub-agents that collectively handle various dialog features. The feature monitor processes and response selector coordinate these specialized sub-agents to achieve unified adaptability across all dialog aspects, making the overall system universally applicable to diverse user preferences.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Manufacturing precision

If the dialog agent generates multiple candidate responses and selects based on global priority objectives, then the alignment with user-defined preferences improves, but the computational resource usage increases

Engineering Contradiction:

Improveresponse alignment precisionVSAvoidcomputational resource usage

Core Design Contradiction:

Manufacturing precisionVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by pre-training specialized machine learning models for each sub-agent on specific dialog features before actual dialog generation. The feature monitor processes also pre-establish evaluation criteria based on global priority objectives, so that during runtime, the selection process can efficiently compare candidate responses against predetermined standards rather than performing complex optimizations from scratch.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system generates multiple candidate responses as temporary objects that are evaluated and discarded after selection. These candidate responses are computationally inexpensive to generate compared to the value of having multiple options to choose from, allowing the system to create and eliminate numerous candidates efficiently during the response selection process.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

3Reliability

If the dialog agent steers dialog towards user-set global priority objectives, then the user interaction quality improves, but the difficulty of detecting and measuring dialog features increases

Engineering Contradiction:

Improvedialog interaction qualityVSAvoiddialog feature measurement difficulty

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The feature monitor processes act as intermediaries between the complex dialog features and the response selection mechanism. These monitors translate difficult-to-measure dialog features (emotion, confusion, personality) into quantifiable values that can be compared against global priority objectives, facilitating reliable decision-making without requiring direct measurement of the underlying complex features.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes parameters by transforming qualitative dialog features into quantitative measurements that can be processed and compared. Each feature monitor process converts abstract concepts like 'emotion' or 'personality' into numerical values that reflect their presence or intensity, enabling the response selector to make objective decisions based on these parameterized representations.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12204854B2System for multi-perspective discourse within a dialog

Publication Date: 2025.01.21 KONINKLIJKE PHILIPS NV

AI summary

Techniques are described for training and/or utilizing sub-agent machine learning models to generate candidate dialog responses. In various implementations, a user-facing dialog agent (202, 302), or another component on its behalf, selects one of the candidate responses which is closest to user defined global priority objectives (318). Global priority objectives can include values (306) for a variety of dialog features such as emotion, confusion, objective-relatedness, personality, verbosity, etc. In various implementations, each machine learning model includes an encoder portion and a decoder portion. Each encoder portion and decoder portion can be a recurrent neural network (RNN) model, such as a RNN model that includes at least one memory layer, such as a long short-term memory (LSTM) layer.