Managing Conversation Uncertainty in TutorJ

Uncertainty in natural language dialogue is often treated through stochastic models. Some of the authors already presented TutorJ mat is an Intelligent Tutoring System, whose interaction with the user is very intensive, and makes use of both dialogic and graphical modality. When managing the interaction, the system needs to cope with uncertainty due to the understanding of the user's needs and wishes. In this paper we present the extended version of TutorJ, focusing on the new features added to its chatbot module. These features allow to merge deterministic and probabilistic reasoning in dialogue management, and in writing the rules of the system's procedural memory

Editorial - Evolving Trends in Supply Chain Management: Complexity, New Technologies, and Innovative Methodological Approaches

Ministerio de Ciencia e Innovación DPI201680750

Building Resilience in Closed-Loop Supply Chains through Information-Sharing Mechanisms

In this paper we reflect on the role of information sharing on increasing the resilience of supply chains. Specifically, we highlight the lack of studies addressing this relevant topic in closed-loop supply chains. Then, we introduce the works covered by the Special Issue “Information Sharing on Sustainable and Resilient Supply Chains” to investigate the relationships between information sharing and resilience in sustainable supply chains.Universidad de Sevilla V PPIT-USDICAR-UniCT (Dpto. Ing. Civil y Arqu. Univ. Catania) Plan de investigación Departamental 2016-201

Insights on Multi-Agent Systems Applications for Supply Chain Management

In this paper, we review relevant literature on the development of multi-agent systems applications for supply chain management. We give a general picture of the state of the art, showing the main applications developed using this novel methodology for analyzing diverse problems in industry. We also analyze generic frameworks for supply chain modelling, showing their main characteristics. We discuss the main topics addressed with this technique and the degree of development of the contributions.Universidad de Sevilla V PPIT-USPiano della Ricerca Dipartimentale 2016-2018 of DICAR-UniC

A meta-cognitive architecture for planning in uncertain environments

Abstract The behavior of an artificial agent performing in a natural environment is influenced by many different pressures and needs coming from both external world and internal factors, which sometimes drive the agent to reach conflicting goals. At the same time, the interaction between an artificial agent and the environment is deeply affected by uncertainty due to the imprecision in the description of the world, and the unpredictability of the effects of the agent's actions. Such an agent needs meta-cognition in terms of both self-awareness and control. Self-awareness is related to the internal conditions that may possibly influence the completion of the task, while control is oriented to performing actions that maintain the internal model of the world and the perceptions aligned. In this work, a general meta-cognitive architecture is presented, which is aimed at overcoming these problems. The proposed architecture describes an artificial agent, which is capable to combine cognition and meta-cognition to solve problems in an uncertain world, while reconciling opposing requirements and goals. While executing a plan, such an agent reflects upon its actions and how they can be affected by its internal conditions, and starts a new goal setting process to cope with unforeseen changes. The work defines the concept of "believability" as a generic uncertain quantity, the operators to manage believability, and provides the reader with the u-MDP that is a novel MDP able to deal with uncertain quantities expressed as possibility, probability, and fuzziness. A couple u-MDPs are used to implement the agent's cognitive and meta-cognitive module. The last one is used to perceive both the physical resources of the agent's embodiment and the actions performed by the cognitive module in order to issue goal setting and re-planning actions

Cebranopadol, a Mixed Opioid Agonist, Reduces Cocaine Self-administration through Nociceptin Opioid and Mu Opioid Receptors

Cocaine addiction is a widespread psychiatric condition still waiting for approved efficacious medications. Previous studies suggested that simultaneous activation of nociceptin opioid (NOP) and mu opioid (MOP) receptors could be a successful strategy to treat cocaine addiction, but the paucity of molecules co-activating both receptors with comparable potency has hampered this line of research. Cebranopadol is a non-selective opioid agonist that at nanomolar concentration activates both NOP and MOP receptors and that recently reached phase-III clinical trials for cancer pain treatment. Here, we tested the effect of cebranopadol on cocaine self-administration (SA) in the rat. We found that under a fixed-ratio-5 schedule of reinforcement, cebranopadol (25 and 50 µg/kg) decreased cocaine but not saccharin SA, indicating a specific inhibition of psychostimulant consumption. In addition, cebranopadol (50 µg/kg) decreased the motivation for cocaine as detected by reduction of the break point measured in a progressive-ratio paradigm. Next, we found that cebranopadol retains its effect on cocaine consumption throughout a 7-day chronic treatment, suggesting a lack of tolerance development toward its effect. Finally, we found that only simultaneous blockade of NOP and MOP receptors by concomitant administration of the NOP antagonist SB-612111 (30 mg/kg) and naltrexone (2.5 mg/kg) reversed cebranopadol-induced decrease of cocaine SA, demonstrating that cebranopadol activates both NOP and classical opioid receptors to exert its effect. Our data, together with the fairly advanced clinical development of cebranopadol and its good tolerability profile in humans, indicate that cebranopadol is an appealing candidate for cocaine addiction treatment

GAIML: A New Language for Verbal and Graphical Interaction in Chatbots

Natural and intuitive interaction between users and complex systems is a crucial research topic in human-computer interaction. A major direction is the definition and implementation of systems with natural language understanding capabilities. The interaction in natural language is often performed by means of systems called chatbots. A chatbot is a conversational agent with a proper knowledge base able to interact with users. Chatbots appearance can be very sophisticated with 3D avatars and speech processing modules. However the interaction between the system and the user is only performed through textual areas for inputs and replies. An interaction able to add to natural language also graphical widgets could be more effective. On the other side, a graphical interaction involving also the natural language can increase the comfort of the user instead of using only graphical widgets. In many applications multi-modal communication must be preferred when the user and the system have a tight and complex interaction. Typical examples are cultural heritages applications (intelligent museum guides, picture browsing) or systems providing the user with integrated information taken from different and heterogenous sources as in the case of the iGoogle™ interface. We propose to mix the two modalities (verbal and graphical) to build systems with a reconfigurable interface, which is able to change with respect to the particular application context. The result of this proposal is the Graphical Artificial Intelligence Markup Language (GAIML) an extension of AIML allowing merging both interaction modalities. In this context a suitable chatbot system called Graphbot is presented to support this language. With this language is possible to define personalized interface patterns that are the most suitable ones in relation to the data types exchanged between the user and the system according to the context of the dialogue

Automatic generation of user interfaces using the set description language

We present a paradigm to generate automatically graphical user interfaces from a formal description of the data model following the well-known model-view-control paradigm. This paradigm provide complete separation between data model and interface description, setting the programmer free from the low-level aspects of programming interfaces, letting him take care of higher level aspects. The interface along with the data model is described by means of a formal language, the Set Description Language. We also describe the infrastructure based on this paradigm we implemented to generate graphical user interfaces for generic applications. Moreover, it can adapt the user interface of a program to the needs derived from the type of data managed by the user from time to time

Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art

Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI