Generic dialogue modeling for multi-application dialogue systems

We present a novel approach to developing interfaces for multi-application dialogue systems. The targeted interfaces allow transparent switching between a large number of applications within one system. The approach, based on the Rapid Dialogue Prototyping Methodology (RDPM) and the Vector Space model techniques from Information Retrieval, is composed of three main steps: (1) producing finalized dia logue models for applications using the RDPM, (2) designing an application interaction hierarchy, and (3) navigating between the applications based on the user's application of interest

COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks

For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues. This paper proposes COTA, a system to improve speed and reliability of customer support for end users through automated ticket classification and answers selection for support representatives. Two machine learning and natural language processing techniques are demonstrated: one relying on feature engineering (COTA v1) and the other exploiting raw signals through deep learning architectures (COTA v2). COTA v1 employs a new approach that converts the multi-classification task into a ranking problem, demonstrating significantly better performance in the case of thousands of classes. For COTA v2, we propose an Encoder-Combiner-Decoder, a novel deep learning architecture that allows for heterogeneous input and output feature types and injection of prior knowledge through network architecture choices. This paper compares these models and their variants on the task of ticket classification and answer selection, showing model COTA v2 outperforms COTA v1, and analyzes their inner workings and shortcomings. Finally, an A/B test is conducted in a production setting validating the real-world impact of COTA in reducing issue resolution time by 10 percent without reducing customer satisfaction

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

USING NS-2 COMPARISON OF GEOGRAPHICAL AND TOPOLOGICAL MULTICAST ROUTING PROTOCOLS ON WIRELESS AD HOC NETWORKS

Performance evaluation of geographical and topological multicast routing algorithms for cellular Wi-Fi ad-hoc networks is offered. Flooding and On-call for Multicast Routing Protocol (ODMRP) are simulated and in comparison with novels protocols: Topological Multicast Routing (ToMuRo) and Geographical Multicast Routing (GeMuRo) in pedestrian and vehicular situations. The situations evaluated recollect one multicast transmitter and one, two and three multicast receivers under numerous mobility and transmission levels. The conduct of 150 nodes is evaluated in terms of cease to end postpone (EED), jitter, packet delivery ratio, and overhead. Consequences display that ToMuRo is suitable for pedestrian eventualities because of its tree-based structure and GeMuRo is right for vehicular situations because its miles based on a mesh topology

Issues in providing a reliable multicast facility

Issues involved in point-to-multipoint communication are presented and the literature for proposed solutions and approaches surveyed. Particular attention is focused on the ideas and implementations that align with the requirements of the environment of interest. The attributes of multicast receiver groups that might lead to useful classifications, what the functionality of a management scheme should be, and how the group management module can be implemented are examined. The services that multicasting facilities can offer are presented, followed by mechanisms within the communications protocol that implements these services. The metrics of interest when evaluating a reliable multicast facility are identified and applied to four transport layer protocols that incorporate reliable multicast

Xcast Based Routing Protocol For Push To Talk Application In Mobile Ad Hoc Networks

Mobile ad-hoc networks comprise a type of wireless network that can be easily created without the need for network infrastructure or administration. These networks are organized and administered into temporary and dynamic network topologies. Unfortunately, mobile ad-hoc networks suffer from some limitations related to insufficient bandwidth. The proliferation of new IP Multimedia subsystem services (IMs), such as Push-to-talk (PTT) applications consume large amounts of bandwidth, resulting in degraded QoS performance of mobile ad-hoc networks. In this thesis, a Priority XCAST based routing protocol (P-XCAST) is proposed for mobile ad-hoc networks to minimize bandwidth consumption. P-XCAST is based on demand route requests and route reply mechanisms for every destination in the PXCAST layer. To build the network topology and fill up the route table for nodes, the information in the route table is used to classify the XCAST list of destinations according to similarities on their next hop. Furthermore, P-XCAST is merged with a proposed Group Management algorithm to handle node mobility by classifying nodes into two types: group head and member. The proposed protocol was tested using the GloMoSim network simulator under different network scenarios to investigate Quality of Service (QoS) performance network metrics. P-XCAST performance was better by about 20% than those of other tested routing protocols by supporting of group size up to twenty receivers with an acceptable QoS. Therefore, it can be applied under different network scenarios (static or dynamic). In addition Link throughput and average delay was calculated using queuing network model; as this model is suitable for evaluating the IEEE 802.11 MAC that is used for push to talk applications. The analytical results for link throughput and average delay were used to validate the simulated results