KNOWLEDGE REPRESENTATION APPROACH TO CLOSED LOOP CONTROL SYSTEM - A TANK SYSTEM CASE-STUDY

Control engineering problems are dealt within a plethora of methods and approaches depending on the a priori knowledge, the description of the process to control, and the main control goal. Classical control theory is mainly based on properties of numerical models. This paper presents an approach that applies to a class of processes described by numerical and logical relations using inference and a knowledge base system. To attain this goal an ontology for control systems is constructed. The work presented in this paper is based in a three tank system benchmark

Multisensor knowledge systems

technical reportWe describe an approach which facilitates and makes explicit the organization of the knowledge necessary to map multisensor system requirements onto an appropriate assembly of algorithms, processors, sensors, and actuators. We have previously introduced the Multisensor Kernel System and Logical Sensor Specifications as a means for high-level specification of multisensor systems. The main goals of such a characterization are: to develop a coherent treatment of multisensor information, to allow system reconfiguration for both fault tolerance and dynamic response to environmental conditions, and to permit the explicit description of control. In this paper we show how Logical Sensors can be incorporated into an object-based approach to the organization of multisensor systems. In particular, we discuss: * a multisensor knowledge base, * a sensor specification scheme, and * a multisensor simulation environment. We give an example application of the system to CAD-based 2-D vision

Discovering logical knowledge in non-symbolic domains

Deep learning and symbolic artificial intelligence remain the two main paradigms in Artificial Intelligence (AI), each presenting their own strengths and weaknesses. Artificial agents should integrate both of these aspects of AI in order to show general intelligence and solve complex problems in real-world scenarios; similarly to how humans use both the analytical left side and the intuitive right side of their brain in their lives. However, one of the main obstacles hindering this integration is the Symbol Grounding Problem [144], which is the capacity to map physical world observations to a set of symbols. In this thesis, we combine symbolic reasoning and deep learning in order to better represent and reason with abstract knowledge. In particular, we focus on solving non-symbolic-state Reinforcement Learning environments using a symbolic logical domain. We consider different configurations: (i) unknown knowledge of both the symbol grounding function and the symbolic logical domain, (ii) unknown knowledge of the symbol grounding function and prior knowledge of the domain, (iii) imperfect knowledge of the symbols grounding function and unknown knowledge of the domain. We develop algorithms and neural network architectures that are general enough to be applied to different kinds of environments, which we test on both continuous-state control problems and image-based environments. Specifically, we develop two kinds of architectures: one for Markovian RL tasks and one for non-Markovian RL domains. The first is based on model-based RL and representation learning, and is inspired by the substantial prior work in state abstraction for RL [115]. The second is mainly based on recurrent neural networks and continuous relaxations of temporal logic domains. In particular, the first approach extracts a symbolic STRIPS-like abstraction for control problems. For the second approach, we explore connections between recurrent neural networks and finite state machines, and we define Visual Reward Machines, an extension to non-symbolic domains of Reward Machines [27], which are a popular approach to non-Markovian RL tasks

The art of HIV elimination: past and present science

Introduction: Remarkable strides have been made in controlling the HIV epidemic, although not enough to achieve epidemic control. More recently, interest in biomedical HIV control approaches has increased, but substantial challenges with the HIV cascade of care hinder successful implementation. We summarise all available HIV prevention methods and make recommendations on how to address current challenges. Discussion: In the early days of the epidemic, behavioural approaches to control the HIV dominated, and the few available evidence-based interventions demonstrated to reduce HIV transmission were applied independently from one another. More recently, it has become clear that combination prevention strategies targeted to high transmission geographies and people at most risk of infections are required to achieve epidemic control. Biomedical strategies such as male medical circumcision and antiretroviral therapy for treatment in HIV-positive individuals and as preexposure prophylaxis in HIV-negative individuals provide immense promise for the future of HIV control. In resourcerich settings, the threat of HIV treatment optimism resulting in increased sexual risk taking has been observed and there are concerns that as ART roll-out matures in resource-poor settings and the benefits of ART become clearly visible, behavioural disinhibition may also become a challenge in those settings. Unfortunately, an efficacious vaccine, a strategy which could potentially halt the HIV epidemic, remains elusive. Conclusion: Combination HIV prevention offers a logical approach to HIV control, although what and how the available options should be combined is contextual. Therefore, knowledge of the local or national drivers of HIV infection is paramount. Problems with the HIV care continuum remain of concern, hindering progress towards the UNAIDS target of 90-90-90 by 2020. Research is needed on combination interventions that address all the steps of the cascade as the steps are not independent of each other. Until these issues are addressed, HIV elimination may remain an unattainable goal

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

Multisensor knowledge systems: interpreting 3-D structure

Journal ArticleWe describe an approach which facilitates and makes explicit the organization of the knowledge necessary to map multisensor system requirements onto an appropriate assembly of algorithms, processors, sensors, and actuators. We have previously introduced the Multisensor Kernel System and Logical Sensor Specifications as a means for high-level specification of multisensor systems. The main goals of such a characterization are: to develop a coherent treatment of multisensor information, to allow system reconfiguration for both fault tolerance and dynamic response to environmental conditions, and to permit the explicit description of control. In this paper we show how Logical Sensors can be incorporated into an object-based approach for the interpretation of 3-D structure. Considering the inherent difficulties in interpreting general configurations of lines in space, and considering the ubiquitousness of special line configurations in man-made environments and objects, we advocate the use of computational units tuned to the occurrence of special configurations. The organized use of these units circumvents the inherent difficulties in interpreting general configurations of lines. After a brief examination of the problem of interpreting general configurations of lines in space, a number of computational units are proposed which are naturally derived from angular relations. The process of propagation (which allows interpretation to spread over the image) is also advocated. Such computational units and processes, which are simple and efficient, can be conveniently organized in a rule-based framework where the occurrence of the various special configurations can be tested. The Multisensor Knowledge System provides such a framework

Using conceptual graphs for clinical guidelines representation and knowledge visualization

The intrinsic complexity of the medical domain requires the building of some tools to assist the clinician and improve the patient’s health care. Clinical practice guidelines and protocols (CGPs) are documents with the aim of guiding decisions and criteria in specific areas of healthcare and they have been represented using several languages, but these are difficult to understand without a formal background. This paper uses conceptual graph formalism to represent CGPs. The originality here is the use of a graph-based approach in which reasoning is based on graph-theory operations to support sound logical reasoning in a visual manner. It allows users to have a maximal understanding and control over each step of the knowledge reasoning process in the CGPs exploitation. The application example concentrates on a protocol for the management of adult patients with hyperosmolar hyperglycemic state in the Intensive Care Unit