{"type":"ScholarlyArticle","title":"Plansformer Tool: Demonstrating Generation of Symbolic Plans Using Transformers","authors":[{"name":"Vishal Pallagani","slug":"vishal-pallagani","given":"Vishal","family":"Pallagani","affiliation":"University of South Carolina - USA"},{"name":"Bharath Muppasani","slug":"bharath-muppasani","given":"Bharath","family":"Muppasani","affiliation":"University of South Carolina - USA"},{"name":"Biplav Srivastava","slug":"biplav-srivastava","given":"Biplav","family":"Srivastava","affiliation":"University of South Carolina - USA"},{"name":"Francesca Rossi","slug":"francesca-rossi","given":"Francesca","family":"Rossi","affiliation":"IBM Research - USA"},{"name":"Lior Horesh","slug":"lior-horesh","given":"Lior","family":"Horesh","affiliation":"IBM Research - USA"},{"name":"Keerthiram Murugesan","slug":"keerthiram-murugesan","given":"Keerthiram","family":"Murugesan","affiliation":"IBM Research - USA"},{"name":"Andrea Loreggia","slug":"andrea-loreggia","given":"Andrea","family":"Loreggia","affiliation":"University of Brescia - Italy"},{"name":"Francesco Fabiano","slug":"francesco-fabiano","given":"Francesco","family":"Fabiano","affiliation":"University of Udine - Italy"},{"name":"Rony Joseph","slug":"rony-joseph","given":"Rony","family":"Joseph","affiliation":"IIIT Naya Raipur - India"},{"name":"Yathin Kethepalli","slug":"yathin-kethepalli","given":"Yathin","family":"Kethepalli","affiliation":"IIIT Naya Raipur - India"}],"abstract":"Plansformer is a novel tool that utilizes a fine-tuned language model based on transformer architecture to generate symbolic plans. Transformers are a type of neural network architecture that have been shown to be highly effective in a range of natural language processing tasks. Unlike traditional planning systems that use heuristic-based search strategies, Plansformer is fine-tuned on specific classical planning domains to generate high-quality plans that are both fluent and feasible. Plansformer takes the domain and problem files as input (in PDDL) and outputs a sequence of actions that can be executed to solve the problem. We demonstrate the effectiveness of Plansformer on a variety of benchmark problems and provide both qualitative and quantitative results obtained during our evaluation, including its limitations. Plansformer has the potential to significantly improve the efficiency and effectiveness of planning in various domains, from logistics and scheduling to natural language processing and human-computer interaction. In addition, we provide public access to Plansformer via a website as well as an API endpoint; this enables other researchers to utilize our tool for planning and execution. The demo video is available at https://youtu.be/_1rlctCGsrk","doi":"10.24963/ijcai.2023/839","url":"https://ijcai.org/proceedings/2023/839","pdf":"https://ijcai.org/proceedings/2023/0839.pdf","markdown":"https://ijcai.org/proceedings/2023/839.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/839","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Demo Track","track":"Demo Track","number":839,"pages":{"first":7158,"last":7162},"keywords":["Planning and Scheduling: PS: Learning in planning and scheduling","Natural Language Processing: NLP: Language generation"],"videos":[],"references":[{"position":1,"raw":"[Aeronautiques et al., 1998] Constructions Aeronautiques, Adele Howe, Craig Knoblock, ISI Drew McDermott, Ash- win Ram, Manuela Veloso, Daniel Weld, David Wilkins SRI, Anthony Barrett, Dave Christianson, et al. 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