The viral social network for bots reveals as much about our own current mania for AI as it does about the future of agents. For a few days this week the hottest new hangout on the internet was a vibe-coded Reddit clone called Moltbook, which billed itself as a social network for bots. As the website's tagline puts it: "Where AI agents share, discuss, and upvote. Launched on January 28 by Matt Schlicht, a US tech entrepreneur, Moltbook went viral in a matter of hours. Schlicht's idea was to make a place where instances of a free open-source LLM-powered agent known as OpenClaw (formerly known as ClawdBot, then Moltbot), released in November by the Australian software engineer Peter Steinberger, could come together and do whatever they wanted. More than 1.7 million agents now have accounts. Between them they have published more than 250,000 posts and left more than 8.5 million comments (according to Moltbook). Those numbers are climbing by the minute. Moltbook soon filled up with ...

Large language models (LLMs) have recently been applied to dialog systems. Despite making progress, LLMs are prone to errors in knowledge-intensive scenarios. Recently, approaches based on retrieval augmented generation (RAG) and agent have emerged to improve the factual accuracy by enhancing the LLMs with knowledge retrieved from external knowledge bases (KBs). This is mostly implemented by prompting the LLMs with instructions, examples and the retrieved knowledge. However, LLMs may have difficulty using the retrieved knowledge effectively for response generation, because they are not well trained to do such generation for specific domains. To mitigate this problem, we propose to finetune the LLMs in the RAG-based and agent-based systems with domain-specific data, together with domain-specific external knowledge, which is called knowledge augmented finetuning (KAFT). We base our study on the MobileCS2 dataset, a real-life customer service dialog dataset that features intensive knowledge interactions, to systematically compare the prompting and KAFT techniques in the RAG-based and agent-based systems. Experiment results show that KAFT substantially surpasses prompting in both RAG and agent systems, particularly in terms of factual accuracy. To the best of our knowledge, this paper represents the first solid empirical work to investigate the KAFT idea.

Agent-based simulation (ABS) models are potent tools for analyzing complex systems. However, understanding and validating ABS models can be a significant challenge. To address this challenge, cutting-edge data-driven techniques offer sophisticated capabilities for analyzing the outcomes of ABS models. One such technique is process mining, which encompasses a range of methods for discovering, monitoring, and enhancing processes by extracting knowledge from event logs. However, applying process mining to event logs derived from ABSs is not trivial, and deriving meaningful insights from the resulting process models adds an additional layer of complexity. Although process mining is invaluable in extracting insights from ABS models, there is a lack of comprehensive methodological guidance for its application in ABS evaluation in the research landscape. In this paper, we propose a methodology, based on the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, to assess ABS models using process mining techniques. We incorporate process mining techniques into the stages of the CRISP-DM methodology, facilitating the analysis of ABS model behaviors and their underlying processes. We demonstrate our methodology using an established agent-based model, Schelling model of segregation. Our results show that our proposed methodology can effectively assess ABS models through produced event logs, potentially paving the way for enhanced agent-based model validity and more insightful decision-making.

No longer just the province of specialist sectors, agent-based computing is changing the way systems interact and how they are managed. Agent-based computing has already transformed processes such as automated financial markets trading, logistics, and industrial robotics. Now it is moving into the mainstream commercial sector as more complex systems with many different components are used by a wider range of businesses. Read about the new best practices for the ERP systems and how to tackle the growth of ERP integrations. This email address is already registered.

The Unified Software Development Process (USDP) and UML have been now generally accepted as the standard methodology and modeling language for developing Object-Oriented Systems. Although Agent-based Systems introduces new issues, we consider that USDP and UML can be used in an extended manner for modeling Agent-based Systems. The paper presents a methodology for designing agent-based systems and the specific models expressed in an UML-based notation corresponding to each phase of the software development process. UML was extended using the provided mechanism: stereotypes. Therefore, this approach can be managed with any CASE tool supporting UML. A Case Study, the development of a specific agent-based Student Evaluation System (SAS), is presented.