The framework is designed to facilitate efficient team dynamics in contexts where both human and AI have operational roles, such as in autonomous vehicles. It determines, based on contextual analysis, whether a human or AI is best suited to perform a task or make a decision at any given time, with the overarching goal of maximizing the performance and minimizing the costs associated with each agent’s operation.

Central to this study is the development of a manager model, based on Reinforcement Learning (RL), that learns to guide delegation decisions. This model is distinctive in its ability to operate in a heterogeneous environment, allowing for the interaction of agents with differing representations of the environment, offering more generalized support for varying team compositions. The manager learns to make optimal delegation decisions through indirect observations of the agents, without access to private or domain-specific knowledge, ensuring a reduction in dependencies between the learning models of the agents and the manager.

The researchers tested this model in a gridworld scenario, a structured environment used for testing, which demonstrated the compatibility and distinctions between agents in specific action spaces and transitions. Here, the manager observes agent transitions and learns to delegate effectively, optimizing the choice of the delegation agent that maximizes the expected reward achieved by the manager, without access to the internal learning mechanisms of the delegated agents.

The results of the study are showing that the manager can perform consistently and effectively, especially with less error-prone agents. The framework was successful in learning desirable delegations and generating good trajectories without direct observation of agent actions, showcasing its potential to train managers successfully in delegating between agents without needing access to additional knowledge.

In conclusion, a paradigm for human-AI collaboration. The manager model developed demonstrates superior performance in making informed delegation decisions amongst agents operating under varied environmental representations. The adaptability and learning capability shown by the manager model in this study point towards a future where the integration of human and AI could significantly enhance collaborative endeavors across different domains.

Check full paper: https://arxiv.org/abs/2309.14718

RECONCILE operates by initiating discussions among multiple agents, each contributing their unique insights and perspectives to the conversation. Each agent, at the outset, generates an individual response to a given problem. A series of structured discussion rounds are held, where agents refine their responses based on the insights shared by their peers, striving to reach a consensus. This process continues until a consensus is achieved, and the final answer is determined through a confidence-weighted voting mechanism among the agents.

This framework is designed to foster diverse thoughts and discussions, allowing each agent to revise their responses in light of insights from other agents, and enabling them to convince their peers to improve their answers. It is implemented with state-of-the-art LLMs such as ChatGPT, Bard, and Claude2 and has demonstrated significant enhancements in the reasoning performance of these agents on various benchmarks, surpassing prior single-agent and multi-agent baselines.

Notably, RECONCILE has also demonstrated its efficacy when implemented with GPT-4, a more advanced model, as one of the agents. In this configuration, RECONCILE not only improved the overall performance of the team of agents but also significantly enhanced the initial accuracy of GPT-4 by an absolute 10.0%. This indicates the potential of the framework to improve even the most advanced models through collaborative discussions and mutual feedback from diverse agents.

The experimental results on multiple reasoning datasets, involving both commonsense and mathematical reasoning, have shown that RECONCILE improves upon prior methods and outperforms GPT-4 on some benchmarks. It has also been observed that RECONCILE achieves better and faster consensus between agents compared to a multi-agent debate baseline, making it a more efficient framework for enhancing the reasoning capabilities of LLMs.

RECONCILE represents a thoughtful approach to solving complex reasoning problems by leveraging diverse insights and external feedback from different model families. It holds promise for future advancements in AI, offering a structured and efficient way to combine the strengths of diverse Large Language Models to achieve refined solutions to complex problems.

Read paper: https://arxiv.org/abs/2309.13007

The primary aim of this research was to resolve the challenges associated with the generalization capabilities of game AI, enabling game agents to make informed and effective decisions beyond their training parameters. Despite the advancements in imitation learning (IL) which has enabled game designers to efficiently guide game agents, there exists a significant impediment related to generalization within this domain.

To address this, the study leverages the principles and successes of data augmentation witnessed in supervised learning. By augmenting the training data to represent real state–action distribution more accurately, the focus was placed predominantly on feature-based state spaces. The study underwent a detailed evaluation of various data augmentation techniques, such as Gaussian noise and scaling, to ascertain their effectiveness across different Game Agents IL settings.
The findings from this research are noteworthy, demonstrating the potential of data augmentation in enhancing the generalization of imitation learning agents. This is vital for progressing research and development in game Agents. The conclusions drawn from this study serve as a significant step forward, offering insights and directions for future research in the field, and highlighting the potential for the development of more adaptive and resilient game agents, without compromising the integrity and depth of the scientific inquiry.

Check full paper: https://arxiv.org/abs/2309.12815

The team has introduced a new algorithm, termed CL-DDQL (Continual Learning-Double Deep Q-Learning), which improves throughput rates and reduces convergence times compared to existing methods. In the CL-DDQL model, multiple User Equipments (UEs) vie for access to various frequency channels, with the algorithm’s intelligent agent designed to make real-time, efficient decisions that maximize throughput by selecting idle time slots. This adaptability is given the high-bandwidth and ever-changing conditions of Metaverse environments.

Extensive numerical simulations were conducted to evaluate the CL-DDQL’s performance, revealing marked improvements in throughput, collision rate, and convergence time. Notably, the algorithm displayed exceptional resilience to frequent context changes, making it highly suitable for dynamic Metaverse applications. Looking ahead, the research team plans to extend their work to non-stationary channels with varying state probability distribution functions and explore its applications in semantically-aware scenarios.

The CL-DDQL algorithm represents a significant step forward in wireless communications, particularly in the complex and dynamic context of the Metaverse. By achieving higher throughputs and shorter convergence times, the research brings us closer to the realization of self-sustaining, highly efficient 6G networks.
Read full paper – https://arxiv.org/abs/2309.10177

MARL’s primary purpose has been to improve rewards through inter-agent cooperation. However, the optimization processes have been found to be compute- and memory-intensive, which impacts the overall speed performance in end-to-end training time.

A recent study delves into the speed performance of MARL, emphasizing the critical metric of latency-bounded throughput in MARL implementations. The research introduces a comprehensive taxonomy of MARL algorithms, categorized by training scheme and communication method. Through this taxonomy, the study evaluates the performance bottlenecks of three state-of-the-art MARL algorithms on a standard multi-core CPU platform.

The report highlights that MARL training can be quite time-intensive. The simulation training process, before deploying a MARL system into its actual physical environment, is particularly lengthy, often spanning days to months. Although there have been efforts to speed up this stage for single-agent RL, MARL poses unique challenges due to the need for inter-agent communications.

A structural categorization of MARL algorithms was proposed based on their computational characteristics. The study revealed that communication, especially in a decentralized setting, is vital in coordinating agent behaviors. Also, the means of communication, whether pre-defined or learned, plays a pivotal role in system efficiency.
The research also made direct comparisons between various MARL algorithms, noting the trade-offs between different categories in terms of communication methods and training schemes. It was observed that algorithms utilizing learnt communication, although superior in some aspects, are more communication-intensive. This calls for specialized acceleration techniques to mitigate these bottlenecks.

In conclusion, the study underscores the importance of considering latency-bounded throughput as a key metric in future MARL research. The growing need for communication in MARL brings about significant overheads, emphasizing the necessity for specialized optimizations and accelerations depending on the algorithm category. Future endeavors in MARL could explore specialized accelerator designs to reduce communication overheads and employ fine-grained task mapping using heterogeneous platforms. Read full paper.

Conversational agents, which encompass text-based agents, VoiceUser interfaces (VUI), and embodied-dialog Agents (EDA), are traditionally built on platforms like Google Dialogflow, Amazon’s Alexa Skills Kit, Cognigy, and Rasa. These agents operate in two primary methodologies: the pipeline and end-to-end methods. LLM-based agents like ChatGPT are representative of the latter.

The paper emphasizes the sequential processing to pipeline-based agents, wherein the natural language understanding (NLU) component processes user messages to discern intent and extract information entities. This data then indicates the dialogue management component’s next action. The response is formulated by the natural language generation (NLG) component.

However, the introduction of end-to-end models like GPT-4, trained on vast datasets, offers a new paradigm. These models infer hidden relationships between input and output utterances, eliminating the need for developers to create interim representations. Yet, they do come with their challenges, including the need for extensive datasets and potential safety issues.

This research is the proposed hybrid approach, by integrating LLMs into pipeline-based agents, the study tells that one can benefit from LLM capabilities, such as generating training data, extracting domain-specific entities, and ensuring localization, without overhauling existing systems.

The researchers conducted hands-on experiments with GPT-4 in the domain of private banking. They explored the model’s proficiency in generating intent lists, producing training data, identifying entities, and even creating synonym lists. With GPT-4 accurately identifying common banking interactions, generating high-quality training data, and even localizing agents across languages and dialects, including the nuanced Swiss German.

The path has privacy concerns, integration challenges, and the sheer complexity of LLMs make a total shift difficult. Hence, the proposed hybrid approach,  technically sophisticated, offers a balanced pathway for businesses.

As the CAI field continues to evolve, the chain between pipeline-based agents and LLMs like GPT-4 will be pivotal. The ongoing research and experimentation in this space shows the potential of this integration, promising a future where conversational agents are not only more efficient but also more contextually aware and human-like. Read full paper here.

These language agents employ LLMs for sequential decision-making, marking a distinct progression in AI. Initial agent designs utilized the LLM directly for action selection. However, the contemporary generation employs a series of LLM interactions to reason or interface with internal memory, thereby refining the decision-making process. The sophisticated nature of these contemporary cognitive language agents underscores the need for a more defined conceptual framework for their characterization and design.

The CoALA framework finds parallels in “production systems” and “cognitive architectures.” Production systems, by design, produce outcomes by applying rules iteratively, an approach that resonates with the challenges LLMs address. Historically, the AI domain adopted these systems to define more complex, structured behaviors, integrating them into cognitive architectures that determined control flows for rule selection, application, and even novel rule generation. The researchers highlight a compelling analogy between production systems and LLMs, positing that controls utilized in production systems could be aptly tailored for LLMs, addressing facets like memory management, grounding, learning, and decisive action.

It offers a holistic approach, emphasizing the parallelism between LLMs and historically significant AI constructs. By proposing this conceptual structure, Princeton’s research team not only underscores current system gaps but also illuminates the path for future advancements, setting the stage for the next generation of grounded, context-aware AI agents.
Check Paper.

One of the main components of the system is its tool-use abilities. This has a structured process of data collection, tool retrieval, tool registration, memory management, and customized model training. It ensures that the integration between LLMs and external APIs is as efficient as possible.

Furthermore, the platform introduces ModelScopeGPT, a specialized instance crafted as an intelligent assistant. It leverages the core capabilities of the framework to connect LLMs with over 1000 AI models and a localized knowledge base from the ModelScope community.

The framework stands out due to its extensive support for diverse tools. Apart from the default tool library, it empowers users to register their personal tools or APIs. This flexibility makes it suitable for a large number of applications, be it for research or commercial purposes.

An interesting use case presented for the ModelScopeGPT highlights its potential. As an integral part of the ModelScope community, this intelligent assistant can facilitate multi-turn conversations and execute API calls. Just a month post its release, it reported handling over 170k requests, demonstrating its robustness and efficacy.
The creators of the framework have ensured that it remains accessible and customizable. Comprehensive documentation is available, making it easier for developers and researchers to understand, adapt, and innovate further.

The launch of the ModelScope-Agent framework could be a turning point for LLMs, expanding their reach and making them more adaptable to real-world scenarios. The open-source nature of this project ensures that the broader AI community can contribute, ensuring continuous improvement and adaptation to emerging needs. To check more, read paper.

Recommender systems have become an integral component of the digital age, playing an important role in areas ranging from e-commerce to entertainment. By analyzing a combination of user preferences, historical interactions, and contextual information, these systems can tailor recommendations to individual users. Yet, despite their domain expertise, these systems have historically fallen short in tasks that require versatile interactions, such as detailed explanations or engaging in fluid conversations.

Conversely, LLMs, exemplified by models like GPT, are celebrated for their advanced conversational capabilities, instruction comprehension, and human-like interactions. Nevertheless, they often lack the depth of domain-specific item knowledge and behavioral patterns essential for specialized fields like online e-commerce.

Addressing these challenges, InteRecAgent has been designed to harness the strengths of both paradigms. The researchers introduced the “Candidate Memory Bus”, a dedicated memory system that efficiently stores current item candidates, ensuring LLMs aren’t overloaded with extensive prompts. This innovation sidesteps the need for LLMs to process lengthy prompts, streamlining the recommendation process.

Moreover, the research introduces a “Plan-first Execution with Dynamic Demonstrations” strategy. This two-phased approach first compels the LLM to devise a comprehensive tool execution plan based on the user’s intent and then proceeds with the execution of the said plan. This structured approach ensures that the LLM can interact effectively with the tools while maintaining the conversational context.

The preliminary findings are promising, suggesting that the InteRecAgent framework can elevate traditional recommender systems. By integrating with LLMs, these systems can become more interactive, featuring a natural language interface that enhances the overall user experience.

While the current research provides a solid foundation, further experiments and refinements are in progress. The team is optimistic about the potential applications of InteRecAgent across various digital platforms and its ability to revolutionize the user-recommendation interaction paradigm. To know more, check out paper.

Stepping into this innovative realm is Abacus.AI with its state-of-the-art platform designed to help users engineer AI agents that closely mirror human intellectual capabilities. Abacus.AI’s robust connector system allows seamless integration with diverse data sources, ensuring that developers can tap into relevant and extensive data lakes and databases. Developers have the liberty to choose from a variety of ML and optimization models, tailoring their AI agents for specific tasks. The platform has a suite of Large Language Models (LLMs), encompassing renowned models like GPT3.5, GPT4, Palm, Azure OpenAI, Claude, and Llama2.

Additionally, Abacus.AI offers its models to users, further broadening the choices available. The capabilities of Abacus.AI’s platform are far-reaching. Developers harnessing this platform can create AI agents adept at navigating the intricate mazes of an organization’s knowledge base to provide precise answers. Moreover, these agents can delve deep into vendor contracts, extracting specific clauses or details on-demand. In the realm of customer support, these AI agents can revolutionize the experience by synergistically combining company knowledge repositories with specific transactional data, ensuring personalized and efficient support. In conclusion, Abacus.AI’s recent platform introduction is a testament to the leaps AI has taken and showcases the transformative power of technology in reshaping the landscape of task automation.