A Temporal Logic Based Approach to Multi-Agent Intrusion Detection and Prevention

Collaborative systems research in the last decade have led to the development in several areas ranging from social computing, e-learning systems to management of complex computer networks. Intrusion Detection Systems (IDS) available today have a number of problems that limit their configurability, scalability or efficiency. An important shortcoming is that the existing architectures is built around a single entity that does most of the data collection and analysis. This work introduces a new architecture for intrusion detection and prevention based on multiple autonomous agents working collectively. We adopt a temporal logic approach to signature-based intrusion detection. We specify intrusion patterns as formulas in a monitorable logic called EAGLE. We also incorporate logics of knowledge into the agents. We implement a prototype tool, called MIDTL and use this tool to detect a variety of security attacks in large log-files provided by DARPA

Implementing Norm-Governed Multi-Agent Systems

The actions and interactions of independently acting agents in a multi-agent system must be managed if the agents are to function effectively in their shared environment. Norms, which define the obligatory, prohibited and permitted actions for an agent to perform, have been suggested as a possible method for regulating the actions of agents. Norms are local rules designed to govern the actions of individual agents whilst also allowing the agents to achieve a coherent global behaviour. However, there appear to be very few instances of norm-governed multi-agent systems beyond theoretical examples. We describe an implementation strategy for allowing autonomous agents to take a set of norms into account when determining their actions. These norms are implemented using directives, which are local rules specifying actions for an agent to perform depending on its current state. Agents using directives are implemented in a simulation test bed, called Sinatra. Using Sinatra, we investigate the ability of directives to manage agent actions. We begin with directives to manage agent interactions. We find that when agents rely on only local rules they will encounter situations where the local rules are unable to achieve the desired global behaviour. We show how a centralised control mechanism can be used to manage agent interactions that are not successfully handled by directives. Controllers, with a global view of the interaction, instruct the individual agents how to act. We also investigate the use of an existing planning tool to implement the resolution mechanism of a controller. We investigate the ability of directives to coordinate the actions of agents in order to achieve a global objective more effectively. Finally, we present a case study of how directives can be used to determine the actions of autonomous mobile robots.Open Acces

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records

Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving. We propose EHRAgent, an LLM agent empowered with a code interface, to autonomously generate and execute code for multi-tabular reasoning within electronic health records (EHRs). First, we formulate an EHR question-answering task into a tool-use planning process, efficiently decomposing a complicated task into a sequence of manageable actions. By integrating interactive coding and execution feedback, EHRAgent learns from error messages and improves the originally generated code through iterations. Furthermore, we enhance the LLM agent by incorporating long-term memory, which allows EHRAgent to effectively select and build upon the most relevant successful cases from past experiences. Experiments on three real-world multi-tabular EHR datasets show that EHRAgent outperforms the strongest baseline by up to 29.6% in success rate. EHRAgent leverages the emerging few-shot learning capabilities of LLMs, enabling autonomous code generation and execution to tackle complex clinical tasks with minimal demonstrations.Comment: Work in Progres

Ways of Interaction of Autonomous Economic Agents in Decentralized Autonomous Organizations

Decentralized Autonomous Organizations (DAO), which have already become independent participants in the relationship in the Web 3.0 economy, are currently neither decentralized nor autonomous because most of the functions and tools used are still centralized, the management of DAOs still largely depends on collective decision-making by all participants. Greater autonomy can be provided by the use of Autonomous Economic Agents (AEA) in DAOs to organize governance, improve communication between participants, create an autonomous reward system, and speed up the search for information and solutions. AEA can be used as a tool to conclude transactions or implement a part of their functions. The article provides an overview of the main technological developments in the field of AEA and DAO and also describes the main ways they could interact in economic peer-to-peer digital systems, taking into account their role, characteristics, and functions. Attention is also drawn to the economic benefits that a DAO acquires from the use of autonomous agents

Recognizing Objects In-the-wild: Where Do We Stand?

The ability to recognize objects is an essential skill for a robotic system acting in human-populated environments. Despite decades of effort from the robotic and vision research communities, robots are still missing good visual perceptual systems, preventing the use of autonomous agents for real-world applications. The progress is slowed down by the lack of a testbed able to accurately represent the world perceived by the robot in-the-wild. In order to fill this gap, we introduce a large-scale, multi-view object dataset collected with an RGB-D camera mounted on a mobile robot. The dataset embeds the challenges faced by a robot in a real-life application and provides a useful tool for validating object recognition algorithms. Besides describing the characteristics of the dataset, the paper evaluates the performance of a collection of well-established deep convolutional networks on the new dataset and analyzes the transferability of deep representations from Web images to robotic data. Despite the promising results obtained with such representations, the experiments demonstrate that object classification with real-life robotic data is far from being solved. Finally, we provide a comparative study to analyze and highlight the open challenges in robot vision, explaining the discrepancies in the performance

Hybrid automata dicretising agents for formal modelling of robots

Some of the fundamental capabilities required by autonomous vehicles and systems for their intelligent decision making are: modelling of the environment and forming data abstractions for symbolic, logic based reasoning. The paper formulates a discrete agent framework that abstracts and controls a hybrid system that is a composition of hybrid automata modelled continuous individual processes. Theoretical foundations are laid down for a class of general model composition agents (MCAs) with an advanced subclass of rational physical agents (RPAs). We define MCAs as the most basic structures for the description of complex autonomous robotic systems. The RPA’s have logic based decision making that is obtained by an extension of the hybrid systems concepts using a set of abstractions. The theory presented helps the creation of robots with reliable performance and safe operation in their environment. The paper emphasizes the abstraction aspects of the overall hybrid system that emerges from parallel composition of sets of RPAs and MCAs