Artificial intelligence and the planning task

The integration of Artificial Intelligence (AI) techniques into various domains has revolutionized numerous industries, and Supply Chain Management (SCM) is no exception. This paper addresses the challenges encountered in SCM and the development of AI solutions within this context. Specifically, we focus on the application of AI in optimizing supply chain planning tasks. This includes forecasting demand, availability and feasibility checks for customer orders, supply chain network design and information flow inside the supply chain planning processes. However, the successful implementation of AI in SCM requires a deep understanding of both the domain-specific challenges and the capabilities and limitations of AI technologies. Thus, this paper proposes an overarching approach that facilitates collaboration between domain experts in SCM and AI experts, enabling them to jointly develop effective solutions.The paper begins by outlining the key challenges faced by SCM professionals, including demand volatility, complexities in inventory management, and dynamic market conditions. Subsequently, it delves into the challenges associated with developing AI solutions for SCM, including data quality, interpretability, and model transparency. To address these challenges, the proposed approach promotes close collaboration and knowledge exchange between SCM and AI experts. By leveraging the domain knowledge and experience of SCM experts, AI experts can better understand the special issues of SCM processes and tailor AI techniques to suit specific needs. In turn, SCM experts can gain insights into the capabilities and limitations of AI, allowing them to make informed decisions regarding the adoption and integration of AI in their supply chain planning operations. Furthermore, the paper discusses the importance of establishing a multidisciplinary team comprising experts from the fields of SCM, AI, and IT. This team-based approach fosters a holistic understanding of SCM challenges and ensures the development of AI solutions that align with business goals and practical constraints.In conclusion, this paper highlights the challenges in combining SCM and AI and proposes a collaborative approach to address these challenges effectively. By leveraging the expertise of both domain and AI experts, organizations can develop tailored AI solutions that enhance supply chain planning, improve decision-making processes, and drive competitive advantage. The proposed approach contributes to the successful integration of AI in SCM, ultimately leading to more efficient and resilient supply chains in the era of artificial intelligence.

근대 이후의 SF 작품은 인간 특유의 탐구심과 호기심을 자극하여 새로운 발명이나 발견을 촉진시키기도 했다. SF작품은 대중들의 공감을 얻기 위해 가까운 미래에 등장할 과학기술의 발전에 대해서 묘사하거나, 과학기술상의 쟁점을 차용하기도 한다. 따라서 SF영화의 시대적 변천을 통해, 과학기술의 대중적 인식에 접근하는 것은 중요한 의의가 있다. 이 논문에서는 인공지능 캐릭터가 본격적으로 등장한 1960년대 이후의 영화를 소재로, 인공지능 캐릭터의 특징을 시기에 따라 정리하고, 현재 벌어지고 있는 낙관론과 비관론에 관련시켜 검토하였다. 1960-80년대 전반기까지의 인공지능은 네트워크에 의존하지 않고 디바이스가 독립적으로 작동하는 형태였다. 그리고 초지능(superintelligence)을 가진 존재가 아니라 한정된 기능에 전문화되어 있었다. 인공지능의 반란은 스스로의 판단에 의해서가 아니라, 인간의 탐욕이나 오류의 결과였다. 1980년대 후반부터는 AGI(범용인공지능) 수준의 능력을 지닌 캐릭터가 등장하였다. 또한 AI를 과신한 나머지, 인간이 AI를 통제할 필요성을 망각하면서 야기되는 오류에 대해서도 문제를 제기하였다. 1990년대에는 인터넷이 보편화되면서 인공지능은 네트워크에 기반한 존재로 묘사되었다. 초인공지능이 등장하여 인간에게 전쟁을 도발하거나, 인공지능이 생명체의 인지능력이나 감정을 동기화시켜 인간성을 말살하는 존재로 묘사되기도 하였다. 영화 속 인공지능은 부정적 측면을 조금 더 부각시킨 것이 사실이다. 인공지능의 오류나 반란을 소재로 한 SF영화가 많기 때문이다. 선한 인공지능 캐릭터를 등장시키더라도, 언제든 인류의 존속을 위협할 수 있는 위험성을 내포한 존재로 묘사되는 경우가 많다. 이는 인공지능이 진실로 인류의 실존을 위협하기 때문이 아니라, 신기술에 대한 막연한 공포감을 이용해 흥행성을 높이는 장치로 인공지능 캐릭터를 창조했기 때문이다. 인류 역사에서 신기술에 대한 공포와 논쟁은 오래 전부터 이어져 왔으며, SF영화의 인공지능 캐릭터는 제작 당시의 과학기술 인식에 의해 상상되었을 뿐이다. 따라서 인공지능을 주제로 한 논쟁에서 영화적 묘사에 집착하기보다는 인류에게 유익한 방향으로 발전을 이끄는 자극제 역할로 국한시키는 것이 필요하다.The Science Fiction(SF) work since post-modern has stimulated a unique curiosity and spirit of inquiry to promote new inventions and discoveries. The SF works describe the development of science and technology that will emerge in the near future or borrow issues on science and technology in order to gain public sympathy. Thus, it is critical to approach the popular perception of science and technology through the transformation of the SF movies. This paper examines the characteristics. This paper summarizes the characteristics of Artificial Intelligence(AI) characters in movies since the 1960s when the AI characters emerged in earnest and examined them in relation to current optimism and pessimism. Until the 1960s and early 1980s, AI was not dependent on the network but operated independently. It was not existed with superintelligence but specialized in limited functions. The revolt of AI was not the result of its self-judgement, but of human greed or error. From the late 1980s, characters with the same level of Artificial General Intelligence (AGI) appeared. Due to the overconfidence of AI, they raised questions about errors caused as human forgets the need to control AI. In the 1990s, the AI was portrayed as existence which was based on network as the internet became popularized. The superintelligence has appeared to provoke war on humans, or AI has been described as one that destroys humanity by synchronizing the cognitive ability and emotions of life. It is true that AI in movies has emphasized its negative aspects. This is because there are many SF movies that are based on AI errors or revolts. Even if a good AI character is introduced, it is often described as a danger that could threaten the continuation of mankind at any time. The reason is that AI is not truly a threat to the existence of mankind, but it was created as a device that enhance popularity by using vague fear of new technology. The fear and debate over new technologies has long been occurred in human history and the AI characters in SF movies have only been imagined by the awareness of science and technology at the time of production. Therefore, it is necessary to limit the AI-themed debate to the role of stimulant that leads to development in a direction beneficial to human rather than focusing on cinematic depictions.

The Prosthetic Impulse Revisited in <em>A.I. Artificial Intelligence</em>

during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access).

The integration of vast volumes of Artificial Intelligence (AI) technology into the built environment is changing the metabolism of urban spaces. Due to the presence of various AIs in urban systems, there are now more agentic forces influencing the trajectory of urban development and entangling with pre-existing biological intelligences. Because of AI's substantial environmental costs, more resources are now needed to satisfy cities’ technological appetite. Urban futures are also becoming more uncertain as private AI companies gain considerable power in urban governance through oligarchic schemes that leave citizens with no voice. In this paper, we bridge Urban Political Ecology (UPE) and urban AI literature, in order to critically examine the nature of AI as it intertwines with urban living and urban infrastructure. More specifically, we offer a threefold contribution to knowledge. First, we examine how the advent of urban AI is altering urban metabolism, zooming in on specific socio-environmental issues pertaining to energy, water and labour. Second, we discuss how the urban metabolisms altered by AI are reproducing uneven dynamics of development that are ultimately leading to different forms of injustice. Third and finally, we propose a potential course of action to politicize urban AI and intervene on its evolution.

Task planning in mobile robotics should be performed efficiently, due to real-time requirements of robot-environment interaction. Its computational efficiency depends both on the number of operators (actions the robot can perform without planning) and the size of the world states (descriptions of the world before and after the application of operators). Thus, in real robotic applications, where both components can be large, planning may turn inefficient, and even unsolvable. In the artificial intelligence (AI) literature on planning, little attention has been put into efficient management of large-scale world descriptions. In real large-scale situations, conventional AI planners (in spite of the most modern improvements) may consume intractable amounts of storage and computing time, due to the huge amount of information. This paper proposes a new approach to task planning called "hierarchical task planning through world abstraction" that, by hierarchically arranging the world representation, becomes a good complement of Stanford Research Institute Problem Solver-style planners, significantly improving their computational efficiency. Broadly speaking, our approach works by first solving the task-planning problem in a highly abstracted model of the environment of the robot, and then refines the solution under more detailed models, where irrelevant world elements can be ignored, due to the results previously obtained at abstracted levels. Among the different implementations that can be made with our general strategy, we describe two that use a graph-based hierarchical world representation named the "annotated and hierarchical" graph. We show experiments, as well as results of a mobile robot operating in a large-scale environment, that demonstrate an important improvement in the efficiency of our algorithms with respect to conventional (both hierarchical and nonhierarchical) planning and their nice integration with existing planners.

Artificial intelligence (AI) is transforming clinical applications, including diagnostics, treatment planning, drug discovery, and administrative tasks. Despite significant progress, AI remains a double-edged sword, and its implementation requires careful, evidence-based evaluation. To date, few AI applications have been fully integrated into clinical workflows, especially in significant populations. This study aims to synthesize evidence on AI utilization in clinical practice, identify key facilitators and barriers, and provide recommendations for implementation within relevant sociocultural and demographic contexts. Following PRISMA guidelines, this review conducted a comprehensive search in Web of Science, Scopus, and PubMed. Bias was assessed using the JBI and NOS tools. Data on study design, population, AI technologies, applications, clinical issues, and outcomes were extracted. Emerging themes were organized using the NASSS framework. Of 1002 records screened, 28 studies were included, most of which were cross-sectional (57%). Machine learning (ML) (43%) was the most frequently used AI technology. AI application outcomes primarily focused on application performance (61%), clinical outcomes (43%), and patient outcomes (32%). Clinical contexts included infectious diseases, chronic conditions, imaging, and physician-patient interactions. Key facilitators included perceptions of operational efficiency, availability of AI tools, confidence in improved accuracy, alignment with goals, perceived cost-saving potential, and enabling environments. Reported barriers involved ethical and privacy concerns, limited user acceptance, inconsistent accuracy, technical complexity, unclear accountability, trust-related issues, and inadequate infrastructure. AI in clinical practice holds tremendous potential in diagnostic accuracy, workflow efficiency, patient engagement, and cost-effectiveness. AI-assisted approaches perform at least as well as conventional methods, even better. Key characteristics within specific contextual settings were synthesized, and contextually informed recommendations were proposed to facilitate AI integration and address the identified barriers. Future research should focus on evaluating AI's long-term impact and addressing emerging issues as AI becomes more embedded in clinical workflows.

Readiness for Artificial Intelligence in Biobanking

A key characteristic of intelligence is the use of ecient problem-solving strategies when faced with unfamiliar tasks. Enabling machines to do autonomous problem-solving is thus a major milestone on the path to developing intelligent systems. Automated planning is a discipline in artificial intelligence research that studies this topic, specifically the process of automatically computing strategies for using actions to achieve a desired outcome. Given a declarative description of a task, a planning system finds an action sequence (a plan) that leads from a given initial state to a state that satisfies a specified goal description. The quality of a plan is measured via its length or, in cost-based planning, via associated costs of the actions it comprises. While the planning problem in general is computationally intractable, many planning tasks can be solved eciently due to some inherent structure of the task. Knowledge about such structure or certain properties of a planning task, so-called control knowledge, can often be extracted automatically from the problem description. This thesis makes several contributions to improve the eciency of automated planning. We focus on forward-chaining heuristic search in the state space of a planning task, currently the most widely used approach to planning. In the first part of this thesis, we detail novel methods for extracting landmarks, a particular type of control knowledge, from planning tasks. We then propose a way of using these landmarks as a heuristic estimator for judging progress during planning, and show empirically that this leads to shorter plans and allows solving more tasks in unit-cost planning. We furthermore analyse the performance gain achieved via landmarks in cost-based planning and find that landmarks can be particularly helpful in this setting, making up for the bad performance of other (cost-sensitive) heuristics. In the second part of this thesis, we focus on improving the underlying search algorithms to increase coverage (the number of tasks solved) and solution quality in planning. We conduct a detailed study of two popular search-control techniques, preferred operators and deferred evaluation, and demonstrate their respective usefulness for improving coverage and solution quality under various conditions. We also consider anytime planning to find high-quality plans given limited time. In anytime planning, the aim is to compute an initial solution quickly, and then iteratively improve on this solution while time remains. We demonstrate that the greediness that is necessary to find an initial plan quickly can impede the planning system in finding better solutions later, unless the system abandons previous eort and restarts the search. We then combine the methods analysed in the previous chapters and incorporate them into one planning system. The resulting planner LAMA, winner of the 2008 International Planning Competition, is presented in detail and compared with other state-of-the art planners. We study the interactions of various techniques employed in the system and show how much each feature contributes to the overall performance. We find that both landmarks and restarting anytime search contribute to the good performance on the set of benchmark tasks considered. Furthermore, the two techniques interact beneficially in some cases. Lastly, we provide an outlook on possible extensions of our work by investigating more complex types of landmarks. We show that using higher-order landmarks can significantly improve the heuristic estimates obtained from a landmark heuristic. However, the additional eort required for finding and using such landmarks does not necessarily pay off.

This paper examines human planning abilities, using as its inspiration planning techniques developed in artificial intelligence. AI research has shown that in certain problems partial‐order planners, which manipulate partial plans while not committing to a particular ordering of those partial plans, are more efficient than total‐order planners, which represent all partial plans as totally ordered. This research asks whether total‐order planning and/or partial‐order planning are accurate descriptions of human planning, and if different populations use different planning techniques. Using a simple planning task modeled after tasks designed in artificial intelligence we tested 7–8 year‐old children, 11–13 year‐old children, adult controls, and adults with damage to the prefrontal cortex. We found that adults and older children exhibited performance on planning tasks of varying complexity which matched that of artificial partial‐order planners, and that this pattern of performance did not vary with multiple presentations of the planning task. In contrast, young children and adults with damage to the prefrontal cortex exhibited performance matching that of artificial total‐order planners. This pattern of performance did vary, however, with multiple presentations of the planning task, with the young children and adults with cortical damage displaying aspects of total‐order planning. In a further study we found that adolescents who had sustained damage to the prefrontal cortex as children displayed two different patterns of performance; when measures of reaction time were analyzed they revealed a pattern of performance suggestive of partial‐order plan representations. However, analyses of the adolescents' protocols revealed a pattern of performance suggestive of total‐order plan representations. The significance of these results to psychology, neuroscience, and artificial intelligence are discussed.

Automated task planning for service robots faces great challenges in handling dynamic domestic environments. Classical methods in the Artificial Intelligence (AI) area mostly focus on relatively structured environments with fewer uncertainties. This work proposes a method to combine semantic knowledge representation with classical approaches in AI to build a flexible framework that can assist service robots in task planning at the high symbolic level. A semantic knowledge ontology is constructed for representing two main types of information: environmental description and robot primitive actions. Environmental knowledge is used to handle spatial uncertainties of particular objects. Primitive actions, which the robot can execute, are constructed based on a STRIPS-style structure, allowing a feasible solution (an action sequence) for a particular task to be created. With the Care-O-Bot (CoB) robot as the platform, we explain this work with a simple, but still challenging, scenario named “get a milk box”. A recursive back-trace search algorithm is introduced for task planning, where three main components are involved, namely primitive actions, world states, and mental actions. The feasibility of the work is demonstrated with the CoB in a simulated environment.

The rapid development of technology has introduced new and groundbreaking approaches to unmanned aerial vehicles (UAVs). One such innovation is the swarm UAV systems. These systems represent a technology where multiple small and often autonomous aircraft collaborate to perform specific tasks. An important component of this system is artificial intelligence. Swarm UAV systems can operate more effectively and coordinatedly by utilising artificial intelligence algorithms. Each UAV is equipped with autonomous control systems and artificial intelligence software. Through data analysis, object recognition, route planning, environmental adaptation and other complex tasks, artificial intelligence aids these UAVs in successfully completing tasks. As a result, swarm UAVs are capable of performing more complex and dynamic missions. The integration of artificial intelligence into swarm UAV systems holds great potential for several application fields. For instance, in search and rescue operations, AI can perform image analysis to detect individuals trapped under debris. In agriculture applications, AI can detect plant diseases or pests and optimize irrigation requirements. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. Additionally, we will focus on the potential future application areas of swarm UAV systems and the potential impacts of this integration. Swarm UAV systems and artificial intelligence represent a significant transformation in the world of technology, and this article will provide a resource to better understand these exciting developments. Keywords: Research of UAVs, integration of artificial intelligence, flight programs, autonomous control.

Motor imagery classification via stacking-based Takagi–Sugeno–Kang fuzzy classifier ensemble

Beside the creative activities in product development, the design process involves multiple routine tasks that are subject to automation. Techniques like knowledge-based engineering, what is commonly understood as the merging of computer-aided design, object-oriented programming and artificial intelligence, have been discussed since years, but have not yet achieved a significant breakthrough. But in particular the actual debate on digitization and artificial intelligence draws much attention on fostering new automation potentials in design of products and services. This article aims at taking an actual snapshot in which fields of application knowledge-based engineering systems and artificial intelligence are used in product development. Therefore, the authors conducted a systematic literature review, limited to scientific literature of the last five years. The literature analysis and synthesis is condensed within a concept matrix that documents actual applications and shows further research potentials.

Knowledge based planning of military engineering tasks