In this work we have proposed to carry out Designing, Developing, and Testing of Multi-Spindle Drilling Head Assembly. Firstly the design of the individual parts MSDH is done using CATIA software with the help of calculated values of diameters obtained the design is done. Secondly the torque and actual thrust of the specific diameters are calculated. Nextly using the analysis values the calculated values are safe and verified. Finally the percentage reduction in the machining time, and cost for conventional machining and our project are tabulated. The main objectives of our project is to design the parts of the multi spindle drilling head and to do a design and power calculation for the spindle and to tabulate the values of the percentage reduction in cost and machining from the calculations obtained.

As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning. Studies in early MR have notably started inquiries into Explainable AI (XAI) -- arguably one of the biggest concerns today for the AI community. Work on explainable MR as well as on MR approaches to explainability in other areas of AI has continued ever since. It is especially potent in modern MR branches, such as argumentation, constraint and logic programming, planning. We hereby aim to provide a selective overview of MR explainability techniques and studies in hopes that insights from this long track of research will complement well the current XAI landscape. This document reports our work in-progress on MR explainability.

Realization of industry-scale, goal-driven, autonomous systems with AI planning technology faces several challenges: flexibly specifying planning goal states in varying situations, synthesizing plans in large state spaces, re-planning in dynamic situations, and facilitating humans to supervise, give feedback and intervene. In this paper, we present Intent-driven Strategic Tactical Planning (ISTP) to address these challenges. We demonstrate its efficacy through its application for radio base station inspection across several locations using drones. The inspection task involves capturing images, thermal images or signal measurements - called knowledge-objects - of various components of the base stations for downstream processing. In the ISTP approach, an operator indicates her goals by flying the drone to different components of interest. These goals are generalized to capture the intent of the operator, which are then instantiated in new situations to generate goals dynamically. Towards planning and re-planning in large state spaces to achieve these goals efficiently, we extend the Strategic-Tactical Planning paradigm. All the components of ISTP are integrated in an intuitive UI and demonstrated through a real life use-case built on the UNITY simulator platform.

The Internet of Things (IoT) is penetrating almost all sectors of the global economy, addressing a wide range of opportunities by applying different Artificial Intelligence (AI) tools to IoT data. Due to the diversity in challenges and applications, IoT solutions are often bespoke and highly domain specific. With the surge of IoT applications, this approach to solutions becomes very costly and time consuming if there is a lack of reusability and replicability across different IoT sectors. This work presents a step towards reusability of IoT solution components applied to Industrial IoT (IIoT). We start from the challenging position of two unique AI-driven applications stemming from two separate IIoT verticals - applications which may be realized using the same components. We identify a set of application independent reusable AI-centric components and show how they can be orchestrated into the unique IoT applications. Our approach shortens the time to market and reduces costs for developing IIoT solutions, and opens a path towards reusability and replicability of IIoT components, thus accelerating the IoT market uptake.

Intelligent Cyber-physical systems can be modeled as multi-agent systems with planning capability to impart adaptivity for changing contexts. In such multi-agent systems, the protocol for plan execution must result in the proper completion and ordering of actions in spite of their distributed execution. However, in untrusted scenarios, there is a possibility of agents not respecting the protocol either due to faults or due to malicious reasons thereby resulting in plan failure. In order to prevent such situations, we propose to implement the execution of agents through smart contracts. This points to a generic architecture seamlessly integrating intelligent planning-based CPS and smart-contracts.

Automated warehouses, as a form of cyber-physical systems (CPSs), require several components to work collaboratively to address the common business objectives of complex logistics systems. During the collaborative operations, a number of key performance indicators (KPI) can be monitored to understand the proficiency of the warehouse and control the operations and decisions. It is possible to drive and monitor these KPIs by looking at both the state of the warehouse components and the operations carried out by them. Therefore, it is necessary to represent this knowledge in an explicit and formally-specified data model and provide automated methods to derive the KPIs from the representation. In this paper, we implement a minimalistic data model for a subset of warehouse resources using linked data in order to monitor a few KPIs, namely sustainability, safety and performance. The applicability of the approach and the data model is illustrated through a use case. We demonstrate that it is possible to develop minimalistic data models through Open Services for Lifecycle Collaboration (OSLC) resource shapes which enables compatibility with the declarative and procedural knowledge of automated warehouse agents specified in Planning Domain Definition Language (PDDL).

In large scale multi-agent systems, both planning for system goals and replanning during plan execution to handle failures are compute-intensive. Since replanning requires faster response time because it happens during plan execution, a lot of focus in the AI planning literature has been on incremental methods, such as plan repair, plan modification etc., which avoid re-synthesizing a complete plan. In this paper, we propose HIPR an architecture and supporting algorithms for fast replanning in multi-agent systems with two fairly general characteristics: (1) where the agents are organized hierarchically based on attributes like location/administration and (2) where most failures are localized i.e. only a few agents are affected by the failure while most of the agents at large remain unaffected. HIPR exploits these characteristics to identify the smallest group of agents that are affected by the failure and to synthesize new plan fragments for only these agents. The localization to smaller number of agents generates smaller replanning problems and hence more efficient solutions. We illustrate application of HIPR on a small, yet realistic route planning use case.

Antifragile systems grow measurably better in the presence of hazards. This is in contrast to fragile systems which break down in the presence of hazards, robust systems that tolerate hazards up to a certain degree, and resilient systems that -- like self-healing systems -- revert to their earlier expected behavior after a period of convalescence. The notion of antifragility was introduced by Taleb for economics systems, but its applicability has been illustrated in biological and engineering domains as well. In this paper, we propose an architecture that imparts antifragility to intelligent autonomous systems, specifically those that are goal-driven and based on AI-planning. We argue that this architecture allows the system to self-improve by uncovering new capabilities obtained either through the hazards themselves (opportunistic) or through deliberation (strategic). An AI planning-based case study of an autonomous wheeled robot is presented. We show that with the proposed architecture, the robot develops antifragile behaviour with respect to an oil spill hazard.

Advancements in sensors and devices have enabled Internet of Things (IoT) adoption in various sectors, especially in domains looking to automate and increase their real-time decision making capabilities to improve efficiencies. Supply chain management in logistics is a perfect fit for adoption of IoT, since it involves shipment of assets being moved, tracked and housed by a number of machines, vehicles and people each day. Smart Contracts are terms and conditions parties can specify that assure trust in the enforceability of the contract and provide visibility at every step of a supply chain. IoT devices can write to a smart contract as a product moves from the factory floor to the store shelves, providing real-time visibility of an enterprises entire supply chain. This paper proposes a smart logistics solution encapsulating smart contracts, logistics planner and condition monitoring of the assets in the Supply Chain Management area. A prototype of the solution is implemented which demonstrates accountability, traceability and liability for asset handling across the supply chain by various parties involved in a logistics scenario.

As IoT devices proliferate, platforms and programming environments to develop IoT-based systems are becoming commonplace. However, the current models of development will soon prove to be inadequate due to the exploding scale, variety and dynamism in the IoT ecosystems, which is making it imperative that these systems manage and operate themselves in an autonomous fashion. Specifically, IoT-based systems must be able to adapt themselves intelligently to changes in the device hardware and software, the context and context-dependent policies and continue delivering to the requirements. Unfortunately, current IoT platforms and programming environments do not have any native support for such intelligence. In order to address this lacuna, we suggest additional components and APIs that can support intelligent autonomy based on the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) architecture. The solution is demonstrated through a couple of concrete case studies implemented using IoT sensors and actuators on Raspberry Pi boards, openHAB - a popular IoT automation environment, Metric-FF - a well-known search-based AI planner and Leshan, an LwM2M platform for providing the sensing and actuation interfaces of the IoT devices.

Service-oriented architecture (SOA) for Cyber PhysicalSystems (CPS) can be implemented through the abstractionof sensing and actuation capabilities of devices as microservicesand providing data processing and decision-making services athigher levels. However, specific to CPS, the critical requirementof adaptivity must be taken into account so that thearchitectures can autonomously handle the dynamic changes inthe devices and their environment making them transparent tothe applications. Towards this, we implement the well-knownMAPE-K (Monitor, Analyze, Plan, Act, using stored Knowledge)reference architecture from autonomic computing, with the corePlan function powered by AI planning techniques. The proof ofconcept prototype is built upon our earlier works: InteropAdapt, a middleware for interoperability of control actions between theapplication layer and the device layer, and HINC, an informationmodel that harmonizes IoT resources spanning devices, networkfunctions and cloud resources. We illustrate our ideas via realisticexamples in the building automation domain.

The Internet of Things (IoT) phenomenon is giving rise to large scale IoT deployments comprising thousands of IoT devices with large degree of heterogeneity. The heterogeneity is further aggravated by dynamism-new applications and requirements, changes in capabilities of devices during their life-cycle and mobility of devices and applications. This is leading to semantic interoperability issues among these devices, which is hindering the ability of system designers to draw the maximum value from these deployments. To address this problem, we present InteropAdapt, an adaptive middleware that can maintain seamless semantic interoperability across dynamic events. The central aim of this paper is to exhibit different architectural elements of InteropAdapt in some detail. We also illustrate InteropAdapt via a simple yet realistic example in the smart office automation domain.

Energy takes about half the operational expenses of data centers making energy conservation a critical goal. Fine-grained control over frequency of processors aids in reducing power at the cost of performance degradation. Placing tasks on servers is formulated as a bin packing and tight packing is considered to be energy conserving as idle servers can be shut down. However, we reveal that due to neglecting task deadline and operating at higher frequency leads to higher energy consumption compared to distributing of tasks over larger number of machines at lower frequencies. In this paper, we explore how to provision tasks in an energy-optimal manner. We show that task-based provisioning is a variable-sized bin packing problem and analyze energy efficiency of 14 classical and proposed heuristics for different distributions of task deadline requirements. We establish that tight packing leads to higher energy usage and we need to consider the task deadline while provisioning. Our heuristics saves as much as 65% energy compared to existing greedy heuristics for an instance of provisioning.

Increasing complexity of Cyber-Physical Systems (CPS) is mandating architectures that are autonomous and adaptive to the changing contexts. The area of planning and acting in Artificial Intelligence (AI) provides a well-understood paradigm for designing such intelligent systems. Capturing the domain knowledge formally, both declarative and procedural is crucial to this approach. There have been many attempts to provide assistance for domain designers to specify and develop high quality domain models. However, there has been no conceptual framework to identify all the necessary enablers for this purpose. Also, almost all the existing tools emphasize on visualization tasks and by using simulation. In this paper, we outline a classification framework based on the 3 C's (correctness, completeness and consistency) of specification and identify the design tasks, motivating them through illustrative snippets. We also report a planning and acting tool integrated with simulation and validation capabilities to support these tasks and present its applications that allows experimenting with domain.

In this paper, we suggest a service-oriented architecture for planning and execution (SOA-PE) in large scale cyber-physical systems (CPS). SOA-PE provides a clean separation between domain modeling, planning, execution, monitoring and actuation services. This approach helps realize the system-of-systems paradigm allowing the decomposition of system goals into smaller subgoals, thus enhancing the scalability of the proposed solution. In addition to supporting large scale, autonomous systems, the service-oriented approach provides several benefits such as reusability, independent development and deployment, platform independence, transparency and flexibility, to the core services of Planning and Execution in these systems. The architecture targets decentralized, multi-agent systems for solutions like smart transportation and logistics and can scale to larger IoT use cases like smart cities. We illustrate the functionalities of the architecture through a prototype implementation and a case study from the logistics domain.

Supporting fast access to large RDF stores has been one of key challenges for enabling use of the Semantic Web in real-life applications, more so in sensor-based systems where large amounts of historic data needs to be stored. We propose a semantics-based temporal view mechanism that enables faster access to time-varying data by caching into memory only the required subset of RDF triples. We describe our experience of implementing such a framework in the context of a wide area network monitoring system. Our preliminary results show that our solution significantly improves client access time and scales well for moderate data sets.

In today's workplace, much information is delivered over the Web, while email remains the workhorse of person-to-person collaboration. Our current mail user agents make it easy to get a web page by clicking on a link in an email, but the reverse link to our email about the web page is missing. To provide this missing link, we developed Sidebar, a plug-in that can run inside both the web browser and the file manager. As the user navigates the Web, or a file system, Sidebar provides a summary of user email that links to the currently displayed document, or folder. In using our software, we've found this basic operation results in many emergent properties that make Sidebar a powerful collaboration tool.