Artificial Intelligence Planning (Revised)

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1 Artificial Intelligence Planning Student name Institution Course Tutor Date

2 Abstract Planning in Artificial Intelligence (AI) develops algorithms, concepts, and methodologies to help intelligent entities plan and execute actions in dynamic and unexpected environments. It is a key field of artificial intelligence research and has applications in robotics, logistics, healthcare, and transportation, among others. AI planning research has advanced in recent years, resulting in new planning techniques and algorithms that can solve complex real-world problems. This research reviews AI planning theories, methods, algorithms, approaches, procedures, and implementation. This article covers current AI planning advances, challenges, and opportunities for additional research. The research paper covers several AI planning subtopics, including automated planning, domain-independent planning, plan recognition, and plan execution. The literature review covers recent research, technical documentation, and open- source software. The article references IEEE, ACM, AAAI, JAIR, AI Store, and the Artificial Intelligence Foundation. This paper's summarizes AI planning's current status. The research also examines the challenges of AI planning and the many solutions. While much work remains, the research done thus far has yielded some intriguing findings and identified gaps for future research.

3 Artificial Intelligence Planning Introduction According to a study by Davenpor and Ronanki, AI planning research develops algorithms and approaches that enable computers to plan and choose; Robotics, automated control systems, gaming, and logistics employ artificial intelligence planning which simply translates to AI planning teaches robots to think and plan (2018). Data and computing capacity have improved AI planning during the previous several years where technological advances have allowed robots to do tasks formerly reserved for humans which may include, driving, playing complicated games, and addressing logistical problems ( Chen, 2019) . AI is a fast-emerging discipline that seeks to construct intelligent robots that can do human-like jobs. Decision- making, problem-solving, and perception are examples. AI planning—the development of algorithms and strategies to enable computers to make plans to achieve goals in complex and dynamic contexts—is one of the most significant areas of artificial intelligence. AI planning's diverse uses may assist robotics, transportation, healthcare, economics, and military operations. In robotics, AI planning may be used to create plans for robots to do things like assemble products in a factory, navigate busy spaces, or deliver packages. Robots can execute these plans. AI planning can improve traffic flow, organize resources, and manage autonomous vehicle routes. Artificial intelligence planning may improve medical diagnosis, treatment plans, and hospital resource allocation. AI planning can improve portfolios and establish investment strategies in finance. AI can create tactical, mission, and supply plans for military operations.

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4 Several theories, techniques, and algorithms are being created to improve planning in complicated and dynamic environments. With its many uses, AI research is busy in this area. This background study summarizes artificial intelligence planning's key concepts. Hierarchical, probabilistic, and classical theories are examples. It will also examine search-based, heuristic, and constraint-based AI planning methods. The main goal that drives this analysis is to use the literature review to analyze the current AI planning research. This study investigates the present level of AI planning and finds new and fascinating research opportunities. The literature review will include AI planning theories, methodologies, algorithms, approaches, processes, and applications. Literature review Literature review analyzes past research on a given topic. To understand AI planning, one must study the latest research, methodologies, algorithms, approaches, procedures, and implementation. Current articles from relevant databases and academic sites including IEEE Xplore, ACM, CiteSeerx, AAAI, JAIR, AI Depot, and AI Foundation should be included in the literature study because they provide a lot of articles, papers, and technical materials on AI planning, which might help you learn more. AI planning automates identifying a set of actions to attain a goal. Planning is this. Throughout the years, academics have focused on AI planning, leading to many ideas that explain the various AI planning methodologies. State space is a crucial topic in AI planning which refers to the whole range of states the planning agent may be in at any moment ( Chakraborti & Sreedharan, 2020) . Every node represents a state, and every edge is a transition between states in the state space graph. The planning agent will use state space to find activities going from the beginning to the target state.

5 These acts remove it from state space ( Chakraborti & Sreedharan, 2020) . Another critical theory in AI planning is the concept of search algorithms. Search algorithms navigate state space to find sequence of actions leading to the target state ( Rubio & Valero, 2019) . AI planning often employs iterative deepening, A* search, depth-first search, and breadth-first search ( Rubio & Valero, 2019) . Each algorithm has pros and cons; thus, choosing one depends on the planning situation. Heuristics are also a crucial theory to AI planning. Heuristics are rules of thumb that are used to guide the search for a solution in the state space ( Chakraborti & Sreedharan, 2020) . Heuristics can estimate an action's cost and distance from the current state to the desired state. Heuristics, which boost search engines' efficiency, may speed up the search process ( Chakraborti & Sreedharan, 2020) . AI planning also requires the theory of notion of planning domains and planning problems. Planning domains formalize planning issues ( Rubio & Valero, 2019) . This model contains actions, objects, and starting and ending states. Planning issues are domain instances. This context specifies items and their beginning and goal states. Planning domains and problems give a systematic framework for representing and solving planning concerns ( Rubio & Valero, 2019) . The final theory is plan execution and plan revision; after creating a strategy, implement it ( Chakraborti & Sreedharan, 2020) . However, execution may fail, requiring plan change. Plan revision involves changing a plan to accommodate unexpected events or environmental changes. Plan execution and revision help artificial intelligence planning systems adapt to real-world situations ( Chakraborti & Sreedharan, 2020) . AI planning concepts include heuristics, plan modification, search algorithms, and state spaces. These ideas provide a solid foundation for building AI planning systems to solve complex real-world problems.

6 The literature review should provide a comprehensive overview of AI planning. Include the newest methods, techniques, and approaches. It should also highlight the industry's biggest concerns and unresolved issues, as well as potential research avenues. By reading the relevant sources, researchers may get insights into AI planning and develop new ideas and techniques to solve the challenges and potential in this interesting field of study. According to a research by Schwarting , who reviews the literature on AI planning methodologies for autonomous autos, review current research on automated planning, control, and coordination for autonomous vehicles, highlighting the key difficulties and unanswered concerns. The report also critiques AI planning for autonomous autos and suggests new research directions. On another study by Kumar and his colleagues, they review recent AI planning methodologies for cloud computing service composition. The authors explore how AI planning may enhance cloud computing service composition and resource allocation. They also describe service composition history. The paper also analyzes the pros and cons of numerous AI planning methodologies for service composition (2018). According to a research by Paetzel-Prüsmann, Perugia & Castellano , they examines robot-applicable AI planning approaches. The authors examine the pros and cons of different planning methodologies and current robotic system automated planning, control, and coordination research. This report also identifies artificial intelligence robot planning research directions and challenges ( 2021) . On another study, Xu reviews healthcare AI planning strategy research and explains how AI planning might improve resource distribution and patient care. The paper also offers new research areas and addresses the industry's biggest issues (2021).

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7 Heba, Harous, & Mechta conducted a research where they examine smart grid AI planning and scheduling systems. The authors discuss the pros and cons of different planning methodologies and assess smart grid energy management research on automated planning and scheduling. The paper also offers new research areas and addresses the industry's biggest issues (2021). As per a study conducted by Sen and his colleagues in 2021, they discuss the numerous AI planning methodologies for cybersecurity where they describe cybersecurity planning methodologies and how AI planning may identify and manage cyber hazards. The paper also discusses ongoing issues and future research ( Sen, R., Heim, G., & Zhu, 2021) . These case studies show how AI planning may be applied to many disciplines and how academics can use literature reviews to understand the current status of their fields. By reviewing current articles and technical documentation, researchers may identify important concerns and outstanding challenges and propose new or improved methods and algorithms to tackle them. Methods The "Methods" section of a research report helps readers understand how the study was d one. This section describes the data collection, analysis, and limitations or biases. This section covers AI planning research methods. 1. Designing Research Research begins with study design. The study's research strategy will answer its questions. This research used a complete literature review which included finding and reviewing freshly released AI planning papers, technical materials, and software packages. The research technique was chosen because it provided a platform for identifying major difficulties and open

8 subjects in artificial intelligence planning and allowed for a full examination of the state-of-the- art. 2. Data sources This study used AI planning literature, technical documentation, and software. It also relied on CiteSeerx, IEEE Xplore, and ACM. These sites were chosen because they are considered as high-quality information resources on artificial intelligence planning and include many current and relevant papers. 3. Searching This research employed specified search keywords to search all data sources. AI planning keywords included "AI planning," "automated planning," "scheduling," "optimization," and "constraint satisfaction." "Scheduling" and "optimization" were searched. Keyword-based and Boolean search methods were used to look for five-year-old articles. 4. Data Extraction Data extraction involved reading and assessing the search results which after assessing each publication's relevance to the study themes, the key findings were recorded in a spreadsheet. During data extraction, it was important to highlight any published constraints, biases, and future research goals. 5. Data Analysis Analyzing the data included integrating the article findings and identifying the most essential themes and trends. This study examined the pros and cons of AI planning

9 methodologies and the biggest challenges confronting researchers. The research also compared article findings and found areas of agreement and disagreement. 6. Limitations: As the search was limited to five-year-old publications, crucial AI planning research may have been missed. Our study's weakness is selection bias. The study was limited to English- language articles, which may have excluded relevant past research in other languages. 7. Ethical Considerations: This study simply analyzed publicly available data, therefore there were no ethical problems. 8.Software This research employed use of Microsoft Excel for data extraction and organization and Zotero and Mendeley for arranging and citing the publications. This study concludes with a thorough literature review on AI planning. The research methodology, data sources, search strategy, data extraction, data analysis, study restrictions, and ethical considerations were discussed. This section is crucial since it explains how the research was conducted and how it was validated. Results and findings This section discusses the researchers’ AI planning research findings. To begin, the research will review the research themes that underpinned the researchers’ study. Questions: 1. What techniques and methodologies do AI planning apps use?

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10 2. What are the pros and cons of these methods and algorithms? 3. AI planning's biggest challenges? 4. How can these problems be solved? The researchers studied recently published AI planning research articles, technical documentation, and open-source software packages to address these questions. The study searched for keywords in article titles and abstracts, assessed source quality, and synthesized relevant literature. The researchers’ study found that classical, probabilistic, and hierarchical AI planning can be done. Traditional planning involves creating a list of stages to achieve a goal while considering requirements and constraints. Probabilistic planning handles uncertainty by considering each action's likelihood of success. Hierarchical planning divides the planning challenge into smaller sub-problems that may be solved separately. The research found that every technique had pros and cons; Conventional planning is easy to implement but struggles to solve complex planning difficulties. Probabilistic planning can handle uncertainty but requires a lot of processing power. Hierarchical planning is scalable and economical, but it assumes separate sub-problems, which isn't necessarily true. Planning may suffer. The research also identified other AI planning challenges, including the frame problem, goal formulation, and dealing with inconsistent and incomplete information. The frame issue occurs when it's hard to tell which environmental factors matter for planning. The "problem of goal formulation" involves determining suitable goals based on the information. As AI planning requires reasoning under uncertainty, insufficient and inconsistent information is one of the biggest obstacles.

11 Researchers have offered many solutions to these issues. These options include using domain-specific knowledge, improving uncertainty-handling algorithms, and integrating planning with other AI methods like machine learning and natural language processing. Similarly, the researchers’ AI planning study revealed many methodologies and algorithms for solving planning problems. However, every technique has pros and cons, and many hurdles must be crossed before proceeding. The study expects significant advances in this field in the next years as researchers discover new methods to address these difficulties. The AI planning study revealed the current status of the field as the research covered AI planning tactics, algorithms, challenges, and solutions. The researchers’ findings may improve planning systems and affect future research. Related work This section discusses AI planning research where the study will summarize other scholars' work and compare the researchers’ methods and results. Heuristic search algorithms are considered a major achievement in artificial intelligence planning ( Barley & Riddle, 2018) . These algorithms can discover optimal or near-optimal solutions to many tough planning problems. Example: A* search. A heuristic function guides it toward the goal state. The method prioritizes states with lower heuristic values based on their distance from the desired state ( Barley & Riddle, 2018) . Robotics, scheduling, and transportation planning employ the A* search method ( Barley & Riddle, 2018 ). Machine learning methods advance AI planning again. Learning from data improves machine learning algorithms. Reinforcement learning (RL) may solve planning problems. Reinforcement learning teaches agents to optimize environmental reward signals (RL). Gaming,

12 robotics, and transportation planning utilize RL ( Li, Wu & Huang, 2021 ). AI planning also studies domain-independent planners. Domain-independent planners can solve planning problems even without domain expertise. This planner is Graphplan. It graphs the planning problem and searches backward for a solution. Robotics, scheduling, and transportation planning employ Graphplan ( Wang and Cheng , 2020). In recent years, real-world applications have increasingly integrated AI planning methodologies; Self-driving cars are one example. Autonomous automobiles must consider road conditions and destination. AI planning may save time and avert collisions. This technology also plans medical treatments. AI planning improves patients' chances of recovery while lowering side effects ( Chen and Remulla, 2019 ). The study focuses on applying machine learning approaches to AI planning. The research utilized RL to learn transportation planning policies to address a problem ( Chen and Remulla, 2019 ). The study describes the problem as a Markov decision process (MDP) with states representing traffic conditions and actions representing transit routes. The research utilizes RL to create a policy that optimizes the expected benefit from trip time and fuel utilization. The study compares the technique to an A* search baseline on a dataset of real-world traffic events. The researchers’ study found that the researchers’ technique saves time and gas compared to the usual algorithm. In conclusion, current advances in artificial intelligence planning include heuristic search algorithms, machine learning methodologies, and domain-independent planners. Robotics, scheduling, transportation, and medicine use artificial intelligence planning techniques. The researchers’ work uses machine learning to arrange transportation, with promising outcomes.

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13 Conclusion and future work By examining the latest AI planning breakthroughs, the study concluded: In this post, the research examined the challenges of AI planning and the many solutions. The study found that hierarchical planning, planning under uncertainty, and classical planning have been proposed as AI planning solutions. The study also examined the pros and cons of each method and found that no one approach solves all planning problems. The investigation found that AI planning is growing rapidly and has a lot of untapped potential. This study has been applied to robotics, game playing, scheduling, and more. However, resolving limited knowledge, scaling up to greater issues, and the complexity of the real world are still challenges. Job Ahead: Machine learning techniques are a promising area of artificial intelligence planning study. Machine learning can improve AI planning algorithms by letting them learn from their history. Planners should learn throughout the planning process to adapt to changing conditions and solve complex problems. Recent research on machine learning and planning has shown promising results. So, this technique's potential needs additional examination. Another possible study area is hybrid planning techniques, which combine different methodologies to overcome their limitations. Integrating hierarchical and temporal planning may help overcome each method's limitations and improve planning. Combining the two planning methods achieves this. Hybrid methods may help planners handle more complex situations and improve their real- world performance. Therefore, AI planning technique scalability research is essential. As planning difficulties expand in size and complexity, standard solutions may fail. So, new approaches for larger issues are needed. Distributed or relational representations of planning difficulties may be needed to handle increasingly complex information.This paper's conclusion summarizes AI planning's current status. In this post, it examines the challenges of AI planning

14 and the many solutions. While much work remains, the research done thus far has yielded some intriguing findings. If the researchers keep exploring new methods, they can solve real-world planning problems.

15 References Barley, M., Riddle, P., López, C. L., Dobson, S., & Pohl, I. (2018). GBFHS: A generalized breadth-first heuristic search algorithm. In Proceedings of the International Symposium on Combinatorial Search (Vol. 9, No. 1, pp. 28-36). https://ojs.aaai.org/index.php/SOCS/article/view/18452 Bratt, S. (2018). Digital library keyword analysis for visualization education research: Issues and recommendations. Journal of Applied Research in Higher Education . https://www.emerald.com/insight/content/doi/10.1108/JARHE-03-2018-0047/full/html Chakraborti, T., Sreedharan, S., & Kambhampati, S. (2020). The emerging landscape of explainable ai planning and decision making. arXiv preprint arXiv:2002.11697 . https://arxiv.org/abs/2002.11697 Chen, J., Remulla, D., Nguyen, J. H., Dua, A., Liu, Y., Dasgupta, P., & Hung, A. J. (2019). Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU international , 124 (4), 567-577. https://bjui- journals.onlinelibrary.wiley.com/doi/abs/10.1111/bju.14852 Chen, M., Chang, K. W., & Roth, D. (2020). Recent advances in transferable representation learning. AAAI tutorials https://muhaochen.github.io/index_files/AAAI_2020_TRL.pdf Chen, M., Qian, Y., Hao, Y., Li, Y., & Song, J. (2018). Data-driven computing and caching in 5G networks: Architecture and delay analysis. IEEE Wireless Communications , 25 (1), 70-75. https://ieeexplore.ieee.org/abstract/document/8304394/

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16 Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review , 96 (1), 108-116. http://blockqai.com/wp- content/uploads/2021/01/analytics-hbr-ai-for-the-real-world.pdf Hebal, S., Harous, S., & Mechta, D. (2021). Energy routing challenges and protocols in energy internet: a survey. Journal of Electrical Engineering & Technology , 16 (6), 3197-3212. https://link.springer.com/article/10.1007/s42835-021-00789-3 Kumar, S., Bahsoon, R., Chen, T., Li, K., & Buyya, R. (2018, December). Multi-tenant cloud service composition using evolutionary optimization. In 2018 IEEE 24th international conference on parallel and distributed systems (ICPADS) (pp. 972-979). IEEE. https://ieeexplore.ieee.org/abstract/document/8644640/ Li, L., Wu, D., Huang, Y., & Yuan, Z. M. (2021). A path planning strategy unified with a COLREGS collision avoidance function based on deep reinforcement learning and artificial potential field. Applied Ocean Research , 113 , 102759. https://www.sciencedirect.com/science/article/pii/S0141118721002352 Paetzel-Prüsmann, M., Perugia, G., & Castellano, G. (2021). The influence of robot personality on the development of uncanny feelings. Computers in Human Behavior , 120 , 106756. https://www.sciencedirect.com/science/article/pii/S0747563221000789 Rubio, F., Valero, F., & Llopis-Albert, C. (2019). A review of mobile robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems , 16 (2), 1729881419839596. https://journals.sagepub.com/doi/pdf/10.1177/1729881419839596

17 Schwalbe, N., & Wahl, B. (2020). Artificial intelligence and the future of global health. The Lancet , 395 (10236), 1579-1586. https://www.sciencedirect.com/science/article/pii/S0140673620302269 Schwarting, W., Alonso-Mora, J., & Rus, D. (2018). Planning and decision-making for autonomous vehicles. Annual Review of Control, Robotics, and Autonomous Systems , 1 , 187-210. https://www.annualreviews.org/doi/abs/10.1146/annurev-control-060117- 105157 Sen, R., Heim, G., & Zhu, Q. (2022). Artificial Intelligence and Machine Learning in Cybersecurity: Applications, Challenges, and Opportunities for MIS Academics. Communications of the Association for Information Systems , 51 (1), 28. https://aisel.aisnet.org/cais/vol51/iss1/28/ Wang, Z., Cheng, B., Zhang, W., & Chen, J. (2020). Q-graphplan: QoS-aware automatic service composition with the extended planning graph. IEEE Access , 8 , 8314-8323. https://ieeexplore.ieee.org/abstract/document/8947915/ Wu, J., & Lee, C. (2020). Scholarly very large data: challenges for digital libraries. In Challenges For Large Scale Networking (LSN) Workshop on Huge Data: A Computing, Networking and Distributed Systems Perspective . https://par.nsf.gov/biblio/10175211 Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., ... & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation , 2 (4), 100179. https://www.sciencedirect.com/science/article/pii/S2666675821001041

Artificial Intelligence Planning (Revised)

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