Knowledge-centered conversational agents with a drive to learn (2024)

Abstract: We create an adaptive conversational agent that assesses the quality of its knowledge and is driven to become more knowledgeable. Unlike agents with predefined tasks, ours can leverage people as diverse sources to meet its knowledge needs. We test the agent in social contexts, where personal and subjective information can be obtained through dialogue. We provide the agent both with generic methods for assessing its knowledge quality (e.g. correctness, completeness, redundancy, interconnectedness, and diversity), as well as with generic capabilities to improve its knowledge by leveraging external sources. We demonstrate that the agent can learn effective policies to acquire the knowledge needed by assessing the efficiency of these capabilities during interaction. Our framework enables on-the-fly learning, offering a dynamic and adaptive approach to shaping conversational interactions.

## introduction machines were initially designed as tools to help people with heavy or repetitive tasks. over time, machines have become more advanced to the point where a proportion can now perform tasks independently. this challenges the societal perception of machines as passive tools and shifts it to consider them active participants performing the task in collaboration with people @xcite . within a hybrid intelligence framework @xcite , people and machine may collaborate as part of a team that is more effective than each individually. with increased autonomy within such collaborative contexts, machines are more likely to encounter unforeseen and complex problems signalled by negative feedback, failure to make decisions, or unsuccessful actions @xcite . to tackle these issues, agents must have the ability to identify problems and evaluate knowledge conditions like missing information, uncertainty, misunderstandings and conflicts. addressing unexpected problems requires adaptability, in the form of leveraging sources of knowledge and information effectively to resolve them. we therefore propose the concept of generic and knowledge-centered agents that 1) can estimate the quality of their current knowledge, in terms of how sufficient it is to service certain needs and 2) have the capacity to actively consult sources of knowledge to become more knowledgeable. unlike traditional task-oriented dialogue (tod) agents (comparison shown in figure 1 ), knowledgeable agents can autonomously determine what they know and do not know, what is the epistemic status of what they know, what they need to learn, and how to acquire that target knowledge. this thesis proposal focuses on dialogue as the way an agent learns and adapts in social context. a flexible learning agent is able to acquire and modify current knowledge through natural language instead of solely relying on (structured) data. this adaptability allows the agent to navigate a dynamic knowledge landscape, making open-domain communication a versatile and practical approach. focusing on machines as social agents capable of qualifying knowledge shared during humanmachine conversations, we position this research in the broader context of conversational agents and specifically within the hi framework in which agents and people are expected to form teams. ## related work dialogue systems task-oriented dialogue (tod) systems (represented as service dialogue systems in figure 1 , left) are designed for specific service tasks, relying on supervised training with user input, dialogue state, and context such as history or user profiles @xcite @xcite . reinforcement learn- tasks: q&a slot-filling recommender … request: → @xmath0: ← answer ← action ← recommendation ← … service dialogue system knowledge-centered dialogue system information: ← subjective knowledge ← personal knowledge ← evolving knowledge ← … query: → missing @xmath1 figure 1 : comparison of dialogue systems: on the left, the conventional service dialogue system (s-ds) undergoes training on static data to subsequently service the needs of users. on the right, a knowledge-centered dialogue system (kc-ds) evaluates its knowledge base and actively participates in dialogue with users to acquire targeted knowledge, which is then integrated into its dynamic knowledge base. adaptive conversational agents several efforts have focused on making dialogue systems flexible to a broader range of use cases, focusing on different domains @xcite @xcite , different tasks @xcite @xcite , or different users @xcite . however, adapting to entirely new tasks poses challenges, requiring generalizability or costly acquisition of domain knowledge. for instance, slotfilling actions lack adaptability in slot types or value ranges @xcite , and recommender systems are constrained by static knowledge @xcite . thus, the support that an agent can provide is inherently limited. making systems more adaptive has been generally studied with different techniques. meta-learning is a data-driven approach that focuses on exposing models to various learning scenarios, so they can extract patterns that can be applied towards novel tasks @xcite . never-ending learning focuses on developing systems that improve continuously as they encounter new data or tasks @xcite . while these acknowledge the shortcomings of static or limited knowledge, these techniques still rely on passive learning, where the system is exposed to certain situations instead of actively searching and prioritizing the learning of specific information. to conclude: these approaches do not determine a need to learn. ## knowledge-centered conversational agents this thesis proposal tackles the research question: "how can conversational agents be equipped to adapt in social collaborative settings by acknowledging and addressing knowledge limitations?". in the next subsections, we address three dimensions of focus and provide specific sub-questions, methodologies and preliminary results. ## conclusion we develop a framework for conversational agents designed to expand its knowledge for a better understanding of the world. the agent does not focus on servicing users in predefined tasks but instead focuses on knowledge that is lacking and needs to be acquired or verified from external sources. our approach is highly flexible, independent to any task-specific goals and capable of handling various dialogue domains without customization effort foot_4 . our framework enables agents to modify task models on the fly and extend domain information, allowing for a dynamic and adaptive approach to shape conversational interactions.