A case-based answer to some problems of knowledge-based systems

2014

Every knowledge-based system has its own knowledge formalism depending on the problem that needs solving, goal to be achieved, and proposed solution. This means the knowledge contained in the system will differ from one system to another. This also means that this knowledge cannot be used by another system, which in turn means every system must start with a learning phase right at the beginning. One of the solutions to overcoming this problem is providing a unified model that can accept all types of knowledge, which guarantees automatic interaction between the knowledge-based systems. Interaction in this paper is defined as knowledge sharing, integration, and transfer from one system to another. This research provides a model and conducts a test on interaction capability. It will help to accelerate the establishment of a new knowledge-based system because it does not need knowledge initialization.

Studies in Computational Intelligence, 2008

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Novel automated interactive reinforcement learning framework with a constraint-based supervisor for procedural tasks, 2024

Learning to perform procedural motion or manipulation tasks in unstructured or uncertain environments poses significant challenges for intelligent agents. Although reinforcement learning algorithms have demonstrated positive results on simple tasks, the hard-to-engineer reward functions and the impractical amount of trialand-error iterations these agents require in long-experience streams still present challenges for deployment in industrially relevant environments. In this regard, interactive reinforcement learning has emerged as a promising approach to mitigate these limitations, whereby a human supervisor provides evaluative or corrective feedback to the learning agent during training. However, the requirement of a human-in-the-loop approach throughout the learning process can be impractical for tasks that span several hours. This study aims to overcome this limitation by automating the learning process and substituting human feedback with an artificial supervisor grounded in constraint-based modeling techniques. In contrast to the logical constraints commonly used for conventional reinforcement learning, constraint-based modeling techniques offer enhanced adaptability in terms of conceptualizing and modeling the human knowledge of a task. This modeling capability allows an automated supervisor to acquire a closer approximation to human reasoning by dividing complex tasks into more manageable components and identifying the associated subtask and contextual cues in which the agent is involved. The supervisor then adjusts the evaluative and corrective feedback to suit the specific subtask under consideration. The framework was assessed using three actor-critic agents in a human-robot interaction environment, demonstrating a sample efficiency improvement of 50% and success rates of ≥95% in simulation and 90% in real-world implementation.

Aha & Muñoz-Avila, 1999

Organizations increasingly need to maintain and share the knowledge of their employees. There are two types of organizational knowledge: product-specific knowledge and skill-specific knowledge. Productspecific knowledge is well known and can be documented in many forms (e.g., user manuals, maintenance manuals, expert systems, and standalone case-based reasoning systems). However, skill-specific knowledge is acquired by the knowledge-workers through experience. The objective of this research is to present a framework in support of sharing knowledge among knowledge-workers. This framework makes use of multi-agent systems to retrieve information from a set of heterogeneous case libraries.

TELKOMNIKA (Telecommunication Computing Electronics and Control), 2014

Every knowledge-based system has its own knowledge formalism depending on the problem that needs solving, goal to be achieved, and proposed solution. This means the knowledge contained in the system will differ from one system to another. This also means that this knowledge cannot be used by another system, which in turn means every system must start with a learning phase right at the beginning. One of the solutions to overcoming this problem is providing a unified model that can accept all types of knowledge, which guarantees automatic interaction between the knowledge-based systems. Interaction in this paper is defined as knowledge sharing, integration, and transfer from one system to another. This research provides a model and conducts a test on interaction capability. It will help to accelerate the establishment of a new knowledge-based system because it does not need knowledge initialization.

2007

In Case Based Reasoning (CBR), knowledge acquisition plays an important role as it allows to progressively improve the system's competencies. One of the approaches of knowledge acquisition consists in performing it while the system is used to solve a problem. An advantage of this strategy is that it is not to constraining for the expert: the system exploits its interactions to acquire pieces of knowledge it needs to solve the current problem and takes the opportunity to learn this new knowledge for future use. In this paper, we present two approaches of interactive knowledge acquisition in CBR. Both approaches rely on the exploitation of reasoning failures. Indeed, an interactive learning process aiming at correcting the solution and at learning new knowledge is triggered when a reasoning failure occurs.

Abstract An increasing amount of work in AI in general, and case-based reasoning in particular, is addressing means of integrating different types of reasoning methods for building and maintaining AI systems. This paper first takes a step back in an attempt to identify a descriptive and comparative framework. It is argued that the knowledge level is the appropriate level for describing the behavior of an intended system, and for identifying its knowledge components and methods.

Journal of the Social History of Alcohol and Drugs

Science, 2010

The 2009 H1N1 swine flu is the first influenza pandemic in decades. The crystal structure of the hemagglutinin from the A/California/04/2009 H1N1 virus shows that its antigenic structure, particularly within the Sa antigenic site, is extremely similar to human H1N1 viruses circulating early in the 20 th century. The co-crystal structure of the 1918 HA with 2D1, an antibody from a survivor of the 1918 Spanish flu that neutralizes both 1918 and 2009 H1N1 viruses, reveals an epitope that is conserved in both pandemic viruses. Thus, antigenic similarity between the 2009 and 1918-like viruses provides an explanation for the age-related immunity to the current influenza pandemic.

In Kitāb al-Muwashshā, a unique work on good manners and high-culture etiquette , al-Washshāʾ (ca. 255–325/869–937) recorded the practices of a group of courtiers and other members of the elite in Abbasid society known as the ẓurafāʾ (" the refined ones "). This group conducted itself according to a strict etiquette (ẓarf) governing dress, posture, speech, and even smell. One of the most interesting practices associated with the ẓurafāʾ is their inscribing of poetry on a variety of objects, from garments, rings, musical instruments, and wine vessels to apples and citron, even on their bodies. This paper examines the practice of inscribing poetry on objects as a unique way of " performing " poetry. In this " refined " practice , poetry was not recited aloud, but rather given a voice by virtue of its physical display in space.