research-article

Situation calculus for controller synthesis in manufacturing systems with first-order state representation

Authors:

Giuseppe De Giacomo,

Sebastian SardiñaAuthors Info & Claims

Volume 302, Issue C

https://doi.org/10.1016/j.artint.2021.103598

Published: 01 January 2022 Publication History

Abstract

Manufacturing is transitioning from a mass production model to a service model in which facilities ‘bid’ to produce products. To decide whether to bid for a complex, previously unseen product, a facility must be able to synthesize, on the fly, a process plan controller that delegates abstract manufacturing tasks in a supplied process recipe to the available manufacturing resources. Often manufacturing processes depend on the data and objects (parts) they produce and consume. To formalize this aspect we need to adopt a first-order representation of the state of the processes. First-order representations of the state are commonly considered in reasoning about action in AI, and here we show that we can leverage the wide literature on the Situation Calculus and ConGolog programs to formalize this kind of manufacturing. With such a formalization available, we investigate how to synthesize process plan controllers in this first-order state setting. We also identify two important decidable cases—finite domains and bounded action theories—for which we provide techniques to actually synthesize the controller.

References

[1]

UK Technology, Strategy Board, A landscape for the future of high value manufacturing in the UK, Tech. rep. 2012.

[2]

C. Rhodes, Manufacturing: Statistics and Policy, Briefing Paper House of Commons Library, 2015.

[3]

X. Xu, From cloud computing to cloud manufacturing, Robot. Comput.-Integr. Manuf. 28 (1) (2012) 75–86.

[4]

Y. Lu, X. Xu, J. Xu, Development of a hybrid manufacturing cloud, J. Manuf. Syst. 33 (4) (2014) 551–566.

[5]

R. Henzel, G. Herzwurm, Cloud manufacturing: A state-of-the-art survey of current issues, in: Procedia CIRP 72, 51st CIRP Conference on Manufacturing Systems, 2018, pp. 947–952.

[6]

O. Fisher, N. Watson, L. Porcu, D. Bacon, M. Rigley, R.L. Gomes, Cloud manufacturing as a sustainable process manufacturing route, J. Manuf. Syst. 47 (2018) 53–68.

[7]

M.P. Groover, Automation, Production Systems, and Computer-Integrated Manufacturing, Prentice Hall Press, 2007.

[8]

G. De Giacomo, F. Patrizi, S. Sardiña, Automatic behavior composition synthesis, Artif. Intell. 196 (2013) 106–142.

[9]

L. de Silva, P. Felli, J.C. Chaplin, B. Logan, D. Sanderson, S. Ratchev, Realisability of production recipes, in: Proceedings of the 22nd European Conference on Artificial Intelligence, ECAI-2016, ECCAI, IOS Press, The Hague, The Netherlands, 2016, pp. 1449–1457.

[10]

P. Felli, B. Logan, S. Sardina, Parallel behavior composition for manufacturing, in: S. Kambhampati (Ed.), Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI 2016, 2016, pp. 271–278.

[11]

P. Felli, L. de Silva, B. Logan, S.M. Ratchev, Process plan controllers for non-deterministic manufacturing systems, in: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, pp. 1023–1030.

[12]

G. De Giacomo, M. Vardi, P. Felli, N. Alechina, B. Logan, Synthesis of orchestrations of transducers for manufacturing, in: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, AAAI-18, AAAI Press, New Orleans, USA, 2018, pp. 6161–6168.

[13]

G. De Giacomo, N. Alechina, T. Brazdil, P. Felli, B. Logan, M. Vardi, Unbounded orchestrations of transducers for manufacturing, in: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, AAAI-19, AAAI Press, Honolulu, USA, 2019.

[14]

O.J. Bakker, J.C. Chaplin, L. de Silva, P. Felli, D. Sanderson, B. Logan, S. Ratchev, Toward process control from formal models of transformable manufacturing systems, in: Procedia CIRP 63, manufacturing Systems 4.0 – Proceedings of the 50th CIRP Conference on Manufacturing Systems, 2017, pp. 521–526.

[15]

L. de Silva, P. Felli, D. Sanderson, J.C. Chaplin, B. Logan, S. Ratchev, Synthesising process controllers from formal models of transformable assembly systems, Robot. Comput.-Integr. Manuf. 58 (2019) 130–144.

[16]

M. Grüninger, C. Menzel, The process specification language (PSL) theory and applications, AI Mag. 24 (2003) 63–74.

[17]

H.J. Levesque, R. Reiter, Y. Lespérance, F. Lin, R.B. Scherl, GOLOG: a logic programming language for dynamic domains, J. Log. Program. 31 (1997) 59–84.

[18]

G. De Giacomo, Y. Lespérance, F. Patrizi, Bounded situation calculus action theories, Artif. Intell. 237 (2016) 172–203.

[19]

B.B. Hariri, D. Calvanese, G. De Giacomo, A. Deutsch, M. Montali, Verification of relational data-centric dynamic systems with external services, in: Proc. of PODS, 2013, pp. 163–174.

[20]

F. Belardinelli, A. Lomuscio, F. Patrizi, Verification of agent-based artifact systems, J. Artif. Intell. Res. 51 (2014) 333–376.

[21]

A. Deutsch, R. Hull, Y. Li, V. Vianu, Automatic verification of database-centric systems, SIGLOG News 5 (2) (2018) 37–56.

[22]

J. McCarthy, P.J. Hayes, Some philosophical problems from the standpoint of artificial intelligence, Mach. Intell. 4 (1969) 463–502.

[23]

R. Reiter, Knowledge in Action. Logical Foundations for Specifying and Implementing Dynamical Systems, The MIT Press, 2001.

[24]

H.J. Levesque, G. Lakemeyer, The Logic of Knowledge Bases, The MIT Press, 2001.

[25]

S. Sardina, G. De Giacomo, Y. Lespérance, H.J. Levesque, On the semantics of deliberation in IndiGolog – From theory to implementation, Ann. Math. Artif. Intell. 41 (2–4) (2004) 259–299.

[26]

S. Abiteboul, R. Hull, V. Vianu, Foundations of databases, Addison-Wesley, Reading, 1995.

Digital Library

[27]

S.-E. Bornscheuer, M. Thielscher, Representing concurrent action and solving conflicts, J. IGPL 3 (4) (1996) 355–368.

[28]

C. Baral, M. Gelfond, Representing concurrent actions in extended logic programming, in: Proceedings of the 13th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI'93, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993, pp. 866–871.

[29]

E. Erdem, V. Patoglu, Applications of action languages in cognitive robotics, in: E. Erdem, J. Lee, Y. Lierler, D. Pearce (Eds.), Correct Reasoning: Essays on Logic-Based AI in Honour of Vladimir Lifschitz, Springer, Berlin, Heidelberg, 2012, pp. 229–246.

[30]

F. Pirri, R. Reiter, Some contributions to the metatheory of the situation calculus, J. ACM 46 (3) (1999) 261–325.

[31]

G. De Giacomo, Y. Lespérance, H.J. Levesque, ConGolog, a concurrent programming language based on the situation calculus, Artif. Intell. 121 (1–2) (2000) 109–169.

[32]

J. Claßen, G. Lakemeyer, A logic for non-terminating Golog programs, in: Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning, KR, 2008, pp. 589–599.

[33]

G. De Giacomo, Y. Lespérance, A.R. Pearce, Situation calculus based programs for representing and reasoning about game structures, in: Principles of Knowledge Representation and Reasoning: Proceedings of the Twelfth International Conference, KR 2010, Toronto, Ontario, Canada, May 9-13, 2010.

[34]

E.M. Clarke, O. Grumberg, D.A. Peled, Model Checking, The MIT Press, Cambridge, MA, USA, 1999.

Digital Library

[35]

D. Calvanese, G. De Giacomo, M. Montali, F. Patrizi, First-order μ-calculus over generic transition systems and applications to the situation calculus, Inf. Comput. 259 (3) (2018) 328–347.

[36]

P. Felli, L. de Silva, B. Logan, S.M. Ratchev, Composite capabilities for cloud manufacturing, in: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, July 10-15, 2018, IFAAMAS, 2018, pp. 1809–1811.

[37]

M. Arenas, J. Baier, J. Navarro, S. Sardina, Incomplete causal laws in the situation calculus using free fluents, in: Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI, New York, USA, 2016, pp. 907–914.

[38]

B. Banihashemi, G. De Giacomo, Y. Lespérance, Abstraction in situation calculus action theories, in: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, 2017, pp. 1048–1055.

[39]

ANSI/ISA, Enterprise-Control System Integration – Part 1: Models and Terminology, ANSI/ISA standard 95.01-2000 (IEC 62264-1 Mod) 2010.

[40]

S. Sardiña, G. De Giacomo, Composition of ConGolog programs, in: IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009, pp. 904–910.

[41]

J.C. Bradfield, I. Walukiewicz, The mu-calculus and model checking, in: Handbook of Model Checking, 2018, pp. 871–919.

[42]

G. De Giacomo, Y. Lespérance, F. Patrizi, S. Sardiña, Verifying ConGolog programs on bounded situation calculus theories, in: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, 2016, pp. 950–956.

[43]

S. Borgo, A. Cesta, A. Orlandini, A. Umbrico, A planning-based architecture for a reconfigurable manufacturing system, in: ICAPS, AAAI Press, 2016, pp. 358–366.

[44]

Z.G. Saribatur, V. Patoglu, E. Erdem, Finding optimal feasible global plans for multiple teams of heterogeneous robots using hybrid reasoning: an application to cognitive factories, Auton. Robots 43 (1) (2019) 213–238.

[45]

A. Ciortea, S. Mayer, F. Michahelles, Repurposing manufacturing lines on the fly with multi-agent systems for the web of things, in: AAMAS, International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, USA / ACM, 2018, pp. 813–822.

[46]

R.F. Kelly, A.R. Pearce, Property persistence in the situation calculus, Artif. Intell. 174 (12–13) (2010) 865–888.

[47]

T. Hofmann, T. Niemueller, J. Claßen, G. Lakemeyer, Continual planning in Golog, in: AAAI, AAAI Press, 2016, pp. 3346–3353.

[48]

L. Xiong, Y. Liu, Strategy representation and reasoning in the situation calculus, in: ECAI, in: Frontiers in Artificial Intelligence and Applications, vol. 285, IOS Press, 2016, pp. 982–990.

[49]

C. Schwering, G. Lakemeyer, M. Pagnucco, Belief revision and projection in the epistemic situation calculus, Artif. Intell. 251 (2017) 62–97.

[50]

M. Arenas, J.A. Baier, J.S. Navarro, S. Sardiña, On the progression of situation calculus universal theories with constants, in: KR, AAAI Press, 2018, pp. 484–493.

[51]

A.M. MacNally, N. Lipovetzky, M. Ramírez, A.R. Pearce, Action selection for transparent planning, in: AAMAS, International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, USA / ACM, 2018, pp. 1327–1335.

[52]

K.E. Baldwin, Autonomous manufacturing systems, in: Proceedings of the IEEE International Symposium on Intelligent Control, 1989, pp. 214–220.

[53]

J. Lee, H.D. Ardakani, S. Yang, B. Bagheri, Industrial big data analytics and cyber-physical systems for future maintenance & service innovation, in: Procedia CIRP 38, Proceedings of the 4th International Conference on Through-Life Engineering Services, 2015, pp. 3–7.

[54]

M. Behandish, S. Nelaturi, J. de Kleer, Automated process planning for hybrid manufacturing, Comput. Aided Des. 102 (2018) 115–127.

Digital Library

[55]

A. Gabaldon, Non-Markovian control in the situation calculus, Artif. Intell. 175 (1) (2011) 25–48.

[56]

P. Mo, N. Li, Y. Liu, Automatic verification of Golog programs via predicate abstraction, in: ECAI, in: Frontiers in Artificial Intelligence and Applications, vol. 285, IOS Press, 2016, pp. 760–768.

[57]

J. Claßen, M. Liebenberg, G. Lakemeyer, B. Zarrieß, Exploring the boundaries of decidable verification of non-terminating Golog programs, in: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada, 2014, pp. 1012–1019.

[58]

J. Claßen, Symbolic verification of Golog programs with First-Order BDDs, in: KR, AAAI Press, 2018, pp. 524–529.

[59]

A. Camacho, C.J. Muise, J.A. Baier, S.A. McIlraith, LTL realizability via safety and reachability games, in: Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018, 2018, pp. 4683–4691.

Cited By

Hofmann TBelle VAgmon NAn BRicci AYeoh W(2023)Abstracting Noisy Robot ProgramsProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598681(534-542)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3598681
Banihashemi BDe Giacomo GLespérance YElkind E(2023)Abstraction of nondeterministic situation calculus action theoriesProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/347(3112-3122)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/347
Adalat OScrimieri DKonur S(2023)Optimal Manufacturing Controller Synthesis Using Situation CalculusArtificial Intelligence XL10.1007/978-3-031-47994-6_19(222-227)Online publication date: 12-Dec-2023
https://dl.acm.org/doi/10.1007/978-3-031-47994-6_19

Index Terms

Situation calculus for controller synthesis in manufacturing systems with first-order state representation

Index terms have been assigned to the content through auto-classification.

Recommendations

Non-terminating processes in the situation calculus
Abstract
By their very design, many robot control programs are non-terminating. This paper describes a situation calculus approach to expressing and proving properties of non-terminating programs expressed in Golog, a high level logic programming language ...
Optimal Manufacturing Controller Synthesis Using Situation Calculus
Artificial Intelligence XL
Abstract
In this paper, we discuss a framework for synthesising manufacturing process controllers using situation calculus, a well-known second-order logic for reasoning about actions in AI. Using a library of high-level ConGolog programs and logical ...
Inductive situation calculus

Temporal reasoning has always been a major test case for knowledge representation formalisms. In this paper, we develop an inductive variant of the situation calculus in ID-logic, classical logic extended with inductive definitions. This logic has been ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Artificial Intelligence

Artificial Intelligence Volume 302, Issue C

Jan 2022

645 pages

ISSN:0004-3702

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers Ltd.

United Kingdom

Publication History

Published: 01 January 2022

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 23 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Hofmann TBelle VAgmon NAn BRicci AYeoh W(2023)Abstracting Noisy Robot ProgramsProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598681(534-542)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3598681
Banihashemi BDe Giacomo GLespérance YElkind E(2023)Abstraction of nondeterministic situation calculus action theoriesProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/347(3112-3122)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/347
Adalat OScrimieri DKonur S(2023)Optimal Manufacturing Controller Synthesis Using Situation CalculusArtificial Intelligence XL10.1007/978-3-031-47994-6_19(222-227)Online publication date: 12-Dec-2023
https://dl.acm.org/doi/10.1007/978-3-031-47994-6_19

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents