195,129 research outputs found
RT-MOVICAB-IDS: Addressing real-time intrusion detection
This study presents a novel Hybrid Intelligent Intrusion Detection System (IDS) known as RT-MOVICAB-IDS that incorporates temporal control. One of its main goals is to facilitate real-time Intrusion Detection, as accurate and swift responses are crucial in this field, especially if automatic abortion mechanisms are running. The formulation of this hybrid IDS combines Artificial Neural Networks (ANN) and Case-Based Reasoning (CBR) within a Multi-Agent System (MAS) to detect intrusions in dynamic computer networks. Temporal restrictions are imposed on this IDS, in order to perform real/execution time processing and assure system response predictability. Therefore, a dynamic real-time multi-agent architecture for IDS is proposed in this study, allowing the addition of predictable agents (both reactive and deliberative). In particular, two of the deliberative agents deployed in this system incorporate temporal-bounded CBR. This upgraded CBR is based on an anytime approximation, which allows the adaptation of this Artificial Intelligence paradigm to real-time requirements. Experimental results using real data sets are presented which validate the performance of this novel hybrid IDSMinisterio de Economía y Competitividad (TIN2010-21272-C02-01, TIN2009-13839-C03-01), Ministerio de Ciencia e Innovación (CIT-020000-2008-2, CIT-020000-2009-12
Circa: The Cooperatice Intelligent Real-Time Control Architecture
The Cooperative Intelligent Real-time Control Architecture (CIRCA)
is a novel architecture for intelligent real-time control that can
guarantee to meet hard deadlines while still using unpredictable,
unrestricted AI methods. CIRCA includes a real-time subsystem used to
execute reactive control plans that are guaranteed to meet the domain's
real-time deadlines, keeping the system safe. At the same time, CIRCA's AI
subsystem performs higher-level reasoning about the domain and the
system's goals and capabilities, to develop future reactive control plans.
CIRCA thus aims to be intelligent about real-time: rather than requiring
the system's AI methods to meet deadlines, CIRCA isolates its reasoning
about which time-critical reactions to guarantee from the actual execution
of the se ected reactions.
The formal basis for CIRCA's performance guarantees is a
state-based world model of agent/environment interactions. Borrowing
approaches from real-time systems research, the world model provides the
information required to make real-time performance guarantees, but avoids
unnecessary complexity. Using the world model, the AI subsystem develops
reactive control plans that restrict the world to a limited set of safe
and desirable states, by guaranteeing the timely performance of actions to
preempt transitions that lead out of the set of states. By executing such
"safe" and "stable" plans, CIRCA's real-time subsystem is able to keep the
system safe (in the world as modeled) for an indeterminate amount of time,
while the parallel AI subsystem develops the next appropriate plan.
We have applied a prototype CIRCA implementation to a simulated
Puma robot arm performing multiple tasks with real-time deadlines, such as
packing parts off a conveyor belt and responding to asynchronous
interrupts. Our experimental results show how the system can guarantee to
accomplish these tasks under a given set of domain conditions (e.g.,
conveyor belt speed) and resource limitations (e.g., robot arm speed).
Furthermore, because CIRCA reasons explicitly about its own predictable,
guaranteed behaviors, the system can recognize when its resources are
insufficient for the desired behaviors (e.g., parts are arriving too
quickly to be packed carefully), and can then make principled
modifications to its performance (e.g., temporarily stacking parts on a
table) to maintain system safety.
(Also cross-referenced as UMIACS-TR-93-104
Reactive Semantic Planning in Unexplored Semantic Environments Using Deep Perceptual Feedback
This paper presents a reactive planning system that enriches the topological representation of an environment with a tightly integrated semantic representation, achieved by incorporating and exploiting advances in deep perceptual learning and probabilistic semantic reasoning. Our architecture combines object detection with semantic SLAM, affording robust, reactive logical as well as geometric planning in unexplored environments. Moreover, by incorporating a human mesh estimation algorithm, our system is capable of reacting and responding in real time to semantically labeled human motions and gestures. New formal results allow tracking of suitably non-adversarial moving targets, while maintaining the same collision avoidance guarantees. We suggest the empirical utility of the proposed control architecture with a numerical study including comparisons with a state-of-the-art dynamic replanning algorithm, and physical implementation on both a wheeled and legged platform in different settings with both geometric and semantic goals.
For more information: Kod*la
PalymSys (TM): An extended version of CLIPS for construction and reasoning using blackboards
This paper describes PalymSys(TM) -- an extended version of the CLIPS language that is designed to facilitate the implementation of blackboard systems. The paper first describes the general characteristics of blackboards and shows how a control blackboard architecture can be used by AI systems to examine their own behavior and adapt to real-time problem-solving situations by striking a balance between domain and control reasoning. The paper then describes the use of PalymSys in the development of a situation assessment subsystem for use aboard Army helicopters. This system performs real-time inferencing about the current battlefield situation using multiple domain blackboards as well as a control blackboard. A description of the control and domain blackboards and their implementation is presented. The paper also describes modifications made to the standard CLIPS 6.02 language in PalymSys(TM) 2.0. These include: (1) a dynamic Dempster-Shafer belief network whose structure is completely specifiable at run-time in the consequent of a PalymSys(TM) rule, (2) extension of the run command including a continuous run feature that enables the system to run even when the agenda is empty, and (3) a built-in communications link that uses shared memory to communicate with other independent processes
A cost-effective intelligent robotic system with dual-arm dexterous coordination and real-time vision
Dexterous coordination of manipulators based on the use of redundant degrees of freedom, multiple sensors, and built-in robot intelligence represents a critical breakthrough in development of advanced manufacturing technology. A cost-effective approach for achieving this new generation of robotics has been made possible by the unprecedented growth of the latest microcomputer and network systems. The resulting flexible automation offers the opportunity to improve the product quality, increase the reliability of the manufacturing process, and augment the production procedures for optimizing the utilization of the robotic system. Moreover, the Advanced Robotic System (ARS) is modular in design and can be upgraded by closely following technological advancements as they occur in various fields. This approach to manufacturing automation enhances the financial justification and ensures the long-term profitability and most efficient implementation of robotic technology. The new system also addresses a broad spectrum of manufacturing demand and has the potential to address both complex jobs as well as highly labor-intensive tasks. The ARS prototype employs the decomposed optimization technique in spatial planning. This technique is implemented to the framework of the sensor-actuator network to establish the general-purpose geometric reasoning system. The development computer system is a multiple microcomputer network system, which provides the architecture for executing the modular network computing algorithms. The knowledge-based approach used in both the robot vision subsystem and the manipulation control subsystems results in the real-time image processing vision-based capability. The vision-based task environment analysis capability and the responsive motion capability are under the command of the local intelligence centers. An array of ultrasonic, proximity, and optoelectronic sensors is used for path planning. The ARS currently has 18 degrees of freedom made up by two articulated arms, one movable robot head, and two charged coupled device (CCD) cameras for producing the stereoscopic views, and articulated cylindrical-type lower body, and an optional mobile base. A functional prototype is demonstrated
Leveraging Large Language Models in Conversational Recommender Systems
A Conversational Recommender System (CRS) offers increased transparency and
control to users by enabling them to engage with the system through a real-time
multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an
unprecedented ability to converse naturally and incorporate world knowledge and
common-sense reasoning into language understanding, unlocking the potential of
this paradigm. However, effectively leveraging LLMs within a CRS introduces new
technical challenges, including properly understanding and controlling a
complex conversation and retrieving from external sources of information. These
issues are exacerbated by a large, evolving item corpus and a lack of
conversational data for training. In this paper, we provide a roadmap for
building an end-to-end large-scale CRS using LLMs. In particular, we propose
new implementations for user preference understanding, flexible dialogue
management and explainable recommendations as part of an integrated
architecture powered by LLMs. For improved personalization, we describe how an
LLM can consume interpretable natural language user profiles and use them to
modulate session-level context. To overcome conversational data limitations in
the absence of an existing production CRS, we propose techniques for building a
controllable LLM-based user simulator to generate synthetic conversations. As a
proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos
built on LaMDA, and demonstrate its fluency and diverse functionality through
some illustrative example conversations
Generating rescheduling knowledge using reinforcement learning in a cognitive architecture
In order to reach higher degrees of flexibility, adaptability and autonomy in manufacturing systems, it is essential to develop new rescheduling methodologies which resort to cognitive capabilities, similar to those found in human beings. Artificial cognition is important for designing planning and control systems that generate and represent knowledge about heuristics for repairbased scheduling. Rescheduling knowledge in the form of decision rules is used to deal with unforeseen events and disturbances reactively in real time, and take advantage of the ability to act interactively with the user to counteract the effects of disruptions. In this work, to achieve the aforementioned goals, a novel approach to generate rescheduling knowledge in the form of dynamic first-order logical rules is proposed. The proposed approach is based on the integration of reinforcement learning with artificial cognitive capabilities involving perception and reasoning/learning skills embedded in the Soar cognitive architecture. An industrial example is discussed showing that the approach enables the scheduling system to assess its operational range in an autonomic way, and to acquire experience through intensive simulation while performing repair tasks.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Generating rescheduling knowledge using reinforcement learning in a cognitive architecture
In order to reach higher degrees of flexibility, adaptability and autonomy in manufacturing systems, it is essential to develop new rescheduling methodologies which resort to cognitive capabilities, similar to those found in human beings. Artificial cognition is important for designing planning and control systems that generate and represent knowledge about heuristics for repairbased scheduling. Rescheduling knowledge in the form of decision rules is used to deal with unforeseen events and disturbances reactively in real time, and take advantage of the ability to act interactively with the user to counteract the effects of disruptions. In this work, to achieve the aforementioned goals, a novel approach to generate rescheduling knowledge in the form of dynamic first-order logical rules is proposed. The proposed approach is based on the integration of reinforcement learning with artificial cognitive capabilities involving perception and reasoning/learning skills embedded in the Soar cognitive architecture. An industrial example is discussed showing that the approach enables the scheduling system to assess its operational range in an autonomic way, and to acquire experience through intensive simulation while performing repair tasks.Sociedad Argentina de Informática e Investigación Operativa (SADIO
- …