Hybrid Temporal Situation Calculus for Planning with Continuous Processes

Mikhail Soutchanski

KR-2022 Tutorial @ FLOC-22:

Sunday, July 31st, 2022, 14:00-15:30, 16:00-17:30

Brief Description
Long Description
Length of the Tutorial
Audience and Prerequisites
Outline
Bibliography
About this document ...

Brief Description

This tutorial focuses on the representation and semantics of change. The basic language is the situation calculus. This tutorial covers both the well-known logical foundations and the recent developments in the situation calculus. Specifically, the initial part of the tutorial includes logical characterizations of qualitative change within the situation calculus, and the middle part presents a recent approach that extends the previous version of the situation calculus with new representations for reasoning about quantitative change over time. It turns out that this new version of temporal situation calculus can serve as a basis for the development of a new lifted heuristic planner that can solve the planning problems in the mixed discrete-continuous domains. It is “lifted” in a sense that it works with action schemata, but not with instantiated actions. These hybrid domains include not only the usual discrete actions and qualitative properties, but also continuous processes that can be possibly initiated or terminated by actions. The final part of the tutorial includes an overview of the experimental results collected from running a tentative implementation of a new planner on selected benchmarks designed by the planning community for the hybrid domains. Since this tutorial focuses on logical representations and semantics, there will be no discussion of the algorithmic or implementation details related to planning. No previous background in the situation calculus or planning is required. However, it is expected that the attendees have basic understanding of knowledge representation and working knowledge of classical first order logic.

Long Description and Motivation

The situation calculus (SC) is a logical approach to representation and reasoning about actions and their effects. It was proposed in [66,67] to capture common sense reasoning patterns of how the actions and events change properties of the world and mental states of the agents. It was inspired by Isaac Newton's ideas [72], and therefore it described change in terms of fluents and inertia [24]. In particular, it was observed that only the specified fluents change their values from one situation to a new situation, but by default most common sense properties are subject to inertia and remain the same in the new situation. The problem of finding a parsimonious characterization of the inertial properties became known as the frame problem by analogy with the fixed coordinate frame. In contrast to Newton, who described change and inertia in terms of quantitative fluents and their fluxions (derivatives), the original SC approached change in purely qualitative terms [67]. This emphasis on the study of the qualitative aspects of change within SC continued for years, e.g. see [46,41], despite a very productive role that the study of continuous change played in the history of science [15,91]. The research papers about a possible integration within SC of reasoning about qualitative and continuous change started to appear only in the middle of the 1990s, e.g., see [23,70] and the references below.

From the very beginning of Artificial Intelligence (AI), it was recognized that the planning problems at the common sense level can be conveniently formulated in SC as the entailment problem in predicate logic. Cordell Green proposed to solve the (planning) problems using resolution-based theorem proving in situation calculus [42,43,44]. Since his program did not work well [82,16], the subsequent planning research switched to more specialized representations such as STRIPS developed by Richard Fikes and Nils Nilsson [32], and ADL, developed by Edwin Pednault [73]. Accordingly, the semantics of planning has changed. Namely, the planning problem was often reformulated as either the satisfiability problem in propositional logic, or as the reachability problem in an instantiated – propositional level – transition system, where both the initial state and the goal state are specified using fluents with explicitly named constants from the planning instance. In 1999, this historical shift was summarized in [98] as follows: “Despite the early formulation of planning as theorem proving [44], most researchers have long assumed that special-purpose planning algorithms are necessary for practical performance".

However, the knowledge representation research of SC continued, since SC is formulated in a well understood many-sorted first order logic (FOL) with the standard Tarskian semantics [83]. This is in contrast to alternative less expressive approaches to reasoning about action that rely on a non-standard syntax and semantics. In particular, Ray Reiter solved the frame problem on a FOL level in SC [83], and in [84] he provided logical foundations for SC and logical theories for reasoning about actions, events and their effects. He proposed Basic Action Theories (BATs) that include precondition axioms, successor state axioms, initial state axioms, unique name axioms, and the foundational axioms for situations. The latter characterize situations as a (finitely) branching tree with the root in the initial situation [78]. Subsequently, Ray Reiter, Hector Levesque [53], their collaborators and other researchers developed several extensions of SC, including sequential temporal SC [92,75,85,50,51,79,34], SC for reasoning about concurrent actions, reasoning about knowledge [89], a systematic axiomatization of direct and indirect effects of actions [54,59,69,61], reasoning about noisy sensors and effects in SC [4,11], decision-theoretic planning in SC [80,14], and the high level robot control [2,45,90,33]. The foundations of this well-established KR&R research are summarized in the Reiter's book [86,77]. It is explained there that the planning problem can be understood as a special case of the entailment problem in SC within the standard FOL semantics [35,86,16].

In Reiter's SC [86], the change is represented using atemporal fluents, i.e., predicates or functions with situational argument, but without explicit time argument. This is somewhat counter-intuitive, since a large body of knowledge about dynamical systems in science and engineering is formulated in terms of mathematical functions that depend on time explicitly and vary over continuous time. Therefore, it is natural to think about a new temporal SC that can represent changes in hybrid systems, where the actions/events switch between situations, but within each situation the specified quantities can change over time. In hybrid automata [71,1,48,3,26,28], actions or events are responsible for discrete transitions between finitely many atomic states, while states may include processes with continuous evolution over time. However, in some practical applications, e.g., a traffic domain representing a large city with flows of cars moving between intersections [94], or the network of electric power generating stations that feed electricity to large regions [74], a hybrid system is more general than a hybrid automaton. These large scale hybrid systems have relational structure, since their states are no longer atomic, but they can be represented using fluents with object parameters. The Hybrid Temporal SC [8,9], in addition to atemporal fluents that are used to represent qualitatively different contexts, introduces temporal fluents that have an explicit time argument. The latter are suitable to represent continuously varying parameterized processes within a state (context). These new representations facilitate reasoning about the hybrid systems with relational structure. There is an ICAPS-2019 paper [10] demonstrating that this new Hybrid Temporal SC can provide a declarative semantics for PDDL+. PDDL+ is the variant of the Planning Domain Definition Language (PDDL), a standardized language developed to address some concerns related to modelling numeric fluents and durative (continuous) actions in PDDL2.1 [36]. Maria Fox and Derek Long mentioned in [37] that it is desirable to have a purely declarative semantics for PDDL+, and the Hybrid Temporal SC addresses this need.

It turns out that one can develop an efficient lifted heuristic planner for PDDL+ domains using the Hybrid Temporal SC [64]. The shift towards general domain-independent planning algorithms based on heuristic search happened in the end of 1990s, e.g., see [68,12,49,17,47,40]. At the same time and more recently, the researchers gradually realized the importance of lifted planning, i.e., that AI planners can efficiently work with action schemata without constructing beforehand a propositional level transition system, e.g., see [21,25,30,31,93,63,65,18,38,6,39,22,99,13] and other references mentioned there. Moreover, Fangzhen Lin developed and implemented a situation calculus based planner R [55,56] that is a variant of the original STRIPS planning algorithm. His planner R did not use heuristics, but it participated in the international competitions on planning at the common-sense level [56,5]. More recently, [7] considered conformant planning in an open-world setting and developed a situation calculus based planner that relies on progression of local-effect action theories [62], where the initial theory is incomplete, but it has a simple syntactic form [52]. However, to the best of our knowledge, with an exception of [81], SC was never taken as a foundation for designing a lifted heuristic planner. Our provisional implementation [64] has been tested on several benchmarks developed to compare the performance of the state of the art PDDL+ planners such as DiNo [76], SMTPlan [19,20], ENHSP [88,87]. The tutorial will briefly review experimental data collected from our tentative implementation and provide comparison with the state-of-the art in PDDL+ planning.

Length of the Tutorial

The tutorial has a duration of 3 hours on July 31st, 2022: the 1st part is from 14:00-15:30, the 2nd part is from 16:00-17:30.

Audience and Prerequisites

This tutorial focuses on the semantics and logical representation for actions and temporally continuous processes in SC. It is expected that the attendees are not familiar with SC and planning, but they have a basic knowledge of first order logic [29]. All algorithmic and implementation issues will be discussed at another venue. The tutorial will include the following topics.

Outline

  1. Introduction to reasoning about action. Intuitive ontology for the situation calculus. Deterministic, primitive, atemporal actions without side-effects [16,53,86,57].
  2. Frame axioms (axioms about lack of effects for actions), the frame problem: Reiter's solution. Effect axioms, normal form for effect axioms. Transforming effect axioms for a given fluent into a single positive effect axiom and a single negative effect axiom for the fluent. Explanation closure, causal completeness, successor state axioms. [83,86]
  3. Foundational axioms for the situation calculus, the tree of situations. Uniform formulas, regressable formulas [78].
  4. Basic Action Theories (BATs): precondition axioms, successor state axioms, initial theory, foundational axioms, unique name axioms (UNA) [78,86]. Closed world assumption (CWA), open world assumption (OWA) [7]. Domain closure assumption (DCA) vs open domains with possibly unspecified objects [35]. State constraints (derived predicates): [54,59]. A compilation approach for acyclic constraints: [69].
  5. The projection and executability problems. A brief review of two techniques for solving these problems: regression (reasoning backwards) [97] and progression (reasoning forward) [95]. Bounded situation calculus theories [27].
  6. Time, instantaneous actions, processes extended in time [86]. The sequential, temporal situation calculus. We discuss (1) new representation that has to be introduced, (2) how the foundational axioms have to be amended, (3) whether we need new axioms.
  7. A recent extension: Hybrid Temporal Situation Calculus (HTSC) [8,9]. Atemporal fluents vs temporal fluents. Continuous processes initiated or terminated by the last action (event). Temporal change axioms (TCA) to represent how temporal numerical fluents continuously change over time in each context. Deriving state evolution axioms (SEA).
  8. Temporal Basic Action Theories in HTSC for sequential actions [8]. Examples. Relation with PDDL+ [10,37].
  9. A lifted heuristic planner based on HTSC [64]. Experimental assessment, and comparison with the other state of the art PDDL+ planners on the challenging benchmarks.
  10. Discussion of possible future research directions.

Bibliography (Web links)

1
Rajeev Alur, Costas Courcoubetis, Nicolas Halbwachs, Thomas A. Henzinger, Pei-Hsin Ho, Xavier Nicollin, Alfredo Olivero, Joseph Sifakis, and Sergio Yovine.
The algorithmic analysis of hybrid systems.
Theor. Comput. Sci., 138(1):3–34, 1995.
2
Eyal Amir.
"Dividing and Conquering Logic".
PhD thesis, Stanford University, Department of Computer Science, 2002.
3
Eugene Asarin, Olivier Bournez, Thao Dang, Oded Maler, and Amir Pnueli.
Effective synthesis of switching controllers for linear systems.
Proceedings of the IEEE, 88(7):1011–1025, 2000.
4
F. Bacchus, J.Y. Halpern, and H.J. Levesque.
Reasoning about noisy sensors and effectors in the situation calculus.
Artificial Intelligence, 111:171–208, 1999.
5
Fahiem Bacchus.
The AIPS '00 planning competition.
AI Mag., 22(3):47–56, 2001.
6
Marcello Balduccini, Daniele Magazzeni, Marco Maratea, and Emily Leblanc.
CASP solutions for planning in hybrid domains.
Theory Pract. Log. Program., 17(4):591–633, 2017.
7
Vitaliy Batusov.
Deterministic Planning in Incompletely Known Domains with Local Effects, Master Thesis.
Technical report, Ryerson University, Department of Copmputer Science, 2014.
8
Vitaliy Batusov, Giuseppe De Giacomo, and Mikhail Soutchanski.
Hybrid Temporal Situation Calculus.
arXiv, 1807.04861, 2018.
9
Vitaliy Batusov, Giuseppe De Giacomo, and Mikhail Soutchanski.
Hybrid temporal situation calculus.
In M-J. Meurs and F. Rudzicz, editors, Advances in Artificial Intelligence - 32nd Canadian Conference on Artif. Intell., Kingston, ON, Canada, May 28-31, 2019, volume 11489 of Lecture Notes in Computer Science, pages 173–185. Springer, 2019.
10
Vitaliy Batusov and Mikhail Soutchanski.
A Logical Semantics for PDDL+.
In J. Benton, Nir Lipovetzky, Eva Onaindia, David E. Smith, and Siddharth Srivastava, editors, Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling, ICAPS 2018, Berkeley, CA, USA, July 11-15, 2019, pages 40–48. AAAI Press, 2019.
11
Vaishak Belle and Hector J. Levesque.
Regression and progression in stochastic domains.
Artif. Intell., 281:103247, 2020.
12
Blai Bonet and Hector Geffner.
Planning as Heuristic Search.
Artif. Intell., 129(1-2):5–33, 2001.
13
Stefan Borgwardt, Jörg Hoffmann, Alisa Kovtunova, and Marcel Steinmetz.
Making DL-Lite Planning Practical.
In Proc. of the 18th Intern. Conf. on Principles of Knowledge Representation and Reasoning, pages 641–645, 11 2021.
14
C. Boutilier, R. Reiter, M. Soutchanski, and S. Thrun.
Decision-theoretic, high-level robot programming in the situation calculus.
In 17th National Conference on Artificial Intelligence (AAAI'00), pages 355–362, Austin, Texas, 2000.
15
Carl Boyer.
The History of the Calculus and Its Conceptual Development.
Dover Publications, 2020.
16
Ronald Brachman and Hector Levesque.
Knowledge Representation and Reasoning.
Morgan Kaufmann, 2004.
17
Daniel Bryce and Subbarao Kambhampati.
A Tutorial on Planning Graph Based Reachability Heuristics.
AI Mag., 28(1):47–83, 2007.
18
Diego Calvanese, Marco Montali, Fabio Patrizi, and Michele Stawowy.
Plan synthesis for knowledge and action bases.
In Subbarao Kambhampati, editor, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, pages 1022–1029. IJCAI/AAAI Press, 2016.
19
Michael Cashmore, Maria Fox, Derek Long, and Daniele Magazzeni.
A compilation of the full PDDL+ language into SMT.
In A.J. Coles, A. Coles, S. Edelkamp, D. Magazzeni, and S. Sanner, editors, 26th Intern. Conf. on Automated Planning and Scheduling, ICAPS 2016, London, UK, June 12-17, 2016, pages 79–87. AAAI Press, 2016.
20
Michael Cashmore, Daniele Magazzeni, and Parisa Zehtabi.
Planning for hybrid systems via satisfiability modulo theories.
J. Artif. Intell. Res., 67:235–283, 2020.
21
Jens Claßen, Yuxiao Hu, and Gerhard Lakemeyer.
A situation-calculus semantics for an expressive fragment of PDDL.
In Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, July 22-26, 2007, Vancouver, British Columbia, Canada, pages 956–961. AAAI Press, 2007.
22
Augusto B. Corrêa, Guillem Francès, Florian Pommerening, and Malte Helmert.
Delete-relaxation heuristics for lifted classical planning.
In Susanne Biundo, Minh Do, Robert Goldman, Michael Katz, Qiang Yang, and Hankz Hankui Zhuo, editors, Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling, ICAPS 2021, Guangzhou, China (virtual), August 2-13, 2021, pages 94–102. AAAI Press, 2021.
23
Tom Costello.
Relating Formalizations of Actions, AAAI 1995 Spring Symposium Series.
In Craig Boutilier and Moises Goldszmidt, editors, Extending Theories of Action: Formal Theory and Practical Applications, SS-95-07, pages 45–50, 1995.
24
Tom Costello and John McCarthy.
Jon Doyle, Extending Mechanics to Minds: The Mechanical Foundations of Psychology and Economics, Cambridge University Press, (2006) .
Artif. Intell., 170(18):1237–1238, 2006.
25
William Cushing, Subbarao Kambhampati, Mausam, and Daniel S. Weld.
When is temporal planning really temporal?
In Manuela M. Veloso, editor, IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007, pages 1852–1859, 2007.
26
J.M. Davoren and A. Nerode.
Logics for hybrid systems (invited paper).
Proceedings of the IEEE, 88(7):985–1010, 2000.
27
Giuseppe De Giacomo, Yves Lespérance, and Fabio Patrizi.
Bounded situation calculus action theories.
Artif. Intell., 237:172–203, 2016.
28
Laurent Doyen, Goran Frehse, George J. Pappas, and André Platzer.
Verification of hybrid systems.
In Edmund M. Clarke, Thomas A. Henzinger, Helmut Veith, and Roderick Bloem, editors, Handbook of Model Checking, pages 1047–1110. Springer, 2018.
29
Herbert B. Enderton.
A Mathematical Introduction to Logic.
Harcourt Press, Second edition, 2001.
30
Esra Erdem, Kadir Haspalamutgil, Can Palaz, Volkan Patoglu, and Tansel Uras.
Combining high-level causal reasoning with low-level geometric reasoning and motion planning for robotic manipulation.
In IEEE International Conference on Robotics and Automation, ICRA 2011, Shanghai, China, 9-13 May 2011, pages 4575–4581. IEEE, 2011.
31
Yi Fan, Minghui Cai, Naiqi Li, and Yongmei Liu.
A First-Order Interpreter for Knowledge-Based Golog with Sensing based on Exact Progression and Limited Reasoning.
In Jörg Hoffmann and Bart Selman, editors, Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, July 22-26, 2012, Toronto, Ontario, Canada. AAAI Press, 2012.
32
Richard Fikes and Nils J. Nilsson.
STRIPS: A new approach to the application of theorem proving to problem solving.
Artif. Intell., 2(3/4):189–208, 1971.
33
Alberto Finzi and Fiora Pirri.
Combining probabilities, failures and safety in robot control.
In Bernhard Nebel, editor, Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI 2001, Seattle, Washington, USA, August 4-10, 2001, pages 1331–1336. Morgan Kaufmann, 2001.
34
Alberto Finzi and Fiora Pirri.
Switching tasks and flexible reasoning in the situation calculus.
Technical Report Technical Reports, vol2, N7, Sapienza Universita Di Roma, Department of Computer and System Sciences Antonio Ruberti, Rome, Italy, March 2010.
E-ISSN 2035-5750.
35
Alberto Finzi, Fiora Pirri, and Ray Reiter.
Open world planning in the situation calculus.
In Proceedings of the 7th Conference on Artificial Intelligence (AAAI-00) and of the 12th Conference on Innovative Applications of Artificial Intelligence (IAAI-00), pages 754–760, Menlo Park, CA, July 30– 3 2000. AAAI Press.
36
Maria Fox and Derek Long.
PDDL2.1: an extension to PDDL for expressing temporal planning domains.
J. Artif. Intell. Res., 20:61–124, 2003.
37
Maria Fox and Derek Long.
Modelling mixed discrete-continuous domains for planning.
J. Artif. Intell. Res., 27:235–297, 2006.
38
Guillem Francès and Hector Geffner.
Effective planning with more expressive languages.
In Subbarao Kambhampati, editor, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, pages 4155–4159. IJCAI/AAAI Press, 2016.
39
Caelan Reed Garrett, Tomás Lozano-Pérez, and Leslie Pack Kaelbling.
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning.
In J. Christopher Beck, Olivier Buffet, Jörg Hoffmann, Erez Karpas, and Shirin Sohrabi, editors, Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling, Nancy, France, October 26-30, 2020, pages 440–448. AAAI Press, 2020.
40
Hector Geffner and Blai Bonet.
A Concise Introduction to Models and Methods for Automated Planning.
Synthesis Lectures on Artificial Intelligence and Machine Learning, 7(2):1–141, 2013.
41
Michael Gelfond, Vladimir Lifschitz, and Arkady Rabinov.
What are the limitations of the situation calculus?
In Robert S. Boyer, editor, Automated Reasoning: Essays in Honor of Woody Bledsoe, Automated Reasoning Series, pages 167–180. Kluwer Academic Publishers, 1991.
42
C. Cordell Green.
Application of theorem proving to problem solving.
In Donald E. Walker and Lewis M. Norton, editors, Proceedings of the 1st International Joint Conference on Artificial Intelligence, Washington, DC, USA, May 7-9, 1969, pages 219–240. William Kaufmann, 1969.
43
C. Cordell Green and Bertram Raphael.
The use of theorem-proving techniques in question-answering systems.
In Richard B. Blue Sr. and Arthur M. Rosenberg, editors, Proceedings of the 23rd ACM National Conference, ACM 1968, USA, 1968, pages 169–181. ACM, 1968.
44
Claude Cordell Green.
"The Application of Theorem Proving to Question-Answering Systems".
PhD thesis, Stanford University, availabale at https://www.kestrel.edu/home/people/green/publications/green-thesis.pdf  https://en.wikipedia.org/wiki/Cordell_Green, 1969.
45
Henrik Grosskreutz and Gerhard Lakemeyer.
cc-Golog – A logical language dealing with continuous change (an AAAI-2000 version is available at https://kbsg.rwth-aachen.de/papers/grosskreutz2000_3.pdf).
Logic Journal of the IGPL, 11(2):179–221, 2003.
http://dx.doi.org/10.1093/jigpal/11.2.179.
46
P. J. Hayes.
The Second Naive Physics Manifesto.
In J. R. Hobbs and R. C. Moore, editors, Formal Theories of the Common-Sense World (Reprinted in Readings in Knowledge Representation, pages 467-485, Ed. by Ronald J. Brachman and Hector J. Levesque), pages 1–36. Norwoord, 1985.
47
Malte Helmert.
The Fast Downward Planning System.
J. Artif. Intell. Res., 26:191–246, 2006.
48
Thomas A. Henzinger.
The theory of hybrid automata.
In Proceedings, 11th Annual IEEE Symposium on Logic in Computer Science, New Brunswick, New Jersey, USA, July 27-30, 1996, pages 278–292. IEEE Computer Society, 1996.
49
Jörg Hoffmann and Bernhard Nebel.
The FF Planning System: Fast Plan Generation Through Heuristic Search.
J. Artif. Intell. Res., 14:253–302, 2001.
50
Todd G. Kelley.
Modeling Complex Systems in the Situation Calculus: A Case Study Using the Dagstuhl Steam Boiler Problem.
In Luigia Carlucci Aiello, Jon Doyle, and Stuart C. Shapiro, editors, Proceedings of the Fifth International Conference on Principles of Knowledge Representation and Reasoning (KR'96), Cambridge, Massachusetts, USA, November 5-8, 1996, pages 26–37. Morgan Kaufmann, 1996.
51
Robert A. Kowalski and Fariba Sadri.
Reconciling the event calculus with the situation calculus.
J. Log. Program., 31(1-3):39–58, 1997.
52
Gerhard Lakemeyer and Hector J. Levesque.
Evaluation-based reasoning with disjunctive information in first-order knowledge bases.
In Dieter Fensel, Fausto Giunchiglia, Deborah L. McGuinness, and Mary-Anne Williams, editors, Proceedings of the Eights International Conference on Principles and Knowledge Representation and Reasoning (KR-02), Toulouse, France, April 22-25, 2002, pages 73–81. Morgan Kaufmann, 2002.
53
H.J. Levesque, F. Pirri, and R. Reiter.
Foundations for the situation calculus.
Linköping Electronic Articles in Computer and Information Science. Available at: http://www.ep.liu.se/ea/cis/1998/018/, vol. 3, N 18, 1998.
54
Fangzhen Lin.
Embracing Causality in Specifying the Indirect Effects of Actions.
In IJCAI-95, available at http://www.cs.toronto.edu/cogrobo/Papers/indtm.pdf, pages 1985–1993, 1995.
55
Fangzhen Lin.
An Ordering on Subgoals for Planning.
Ann. Math. Artif. Intell., 21(2-4):321–342, 1997.
56
Fangzhen Lin.
A planner called R, source code is available at http://www.cs.ust.hk/faculty/flin/programs/plannerR1-1.tar.gz.
AI Mag., 22(3):73–76, 2001.
57
Fangzhen Lin.
Situation calculus.
In Frank van Harmelen, Vladimir Lifschitz, and Bruce W. Porter, editors, Handbook of Knowledge Representation, volume 3 of Foundations of Artificial Intelligence, pages 649–669. Elsevier, 2008.
58
Fangzhen Lin and Ray Reiter.
Forget It!
In Proceedings of the AAAI Fall Symposium on Relevance, pages 154–159, 1994.
59
Fangzhen Lin and Raymond Reiter.
State constraints revisited, available at http://www.cs.toronto.edu/cogrobo/Papers/constraint.pdf.
J. Log. Comput., 4(5):655–678, 1994.
60
Fangzhen Lin and Raymond Reiter.
How to Progress a Database.
Artificial Intelligence, 92:131–167, 1997.
61
Fangzhen Lin and Mikhail Soutchanski.
Causal theories of actions revisited.
In Wolfram Burgard and Dan Roth, editors, Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, California, USA, August 7-11, 2011. AAAI Press, 2011.
62
Yongmei Liu and Gerhard Lakemeyer.
On First-Order Definability and Computability of Progression for Local-Effect Actions and Beyond.
In Craig Boutilier, editor, IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009, pages 860–866, 2009.
63
Arman Masoumi, Megan Antoniazzi, and Mikhail Soutchanski.
Modeling organic chemistry and planning organic synthesis.
In G. Gottlob, G. Sutcliffe, and A. Voronkov, editors, Global Conference on Artificial Intelligence, GCAI 2015, Tbilisi, Georgia, October 16-19, 2015, volume 36 of EPiC Series in Computing, pages 176–195. EasyChair, 2015.
64
Shaun Mathew.
Heuristic Planning for Continuous Systems In Hybrid Temporal Situation Calculus, Master Thesis.
Technical report, Ryerson University, Department of Copmputer Science, Sep 2021.
65
Rami Matloob and Mikhail Soutchanski.
Exploring organic synthesis with state-of-the-art planning techniques.
In Scheduling and Planning Applications woRKshop (SPARK) at the 26th ICAPS, London, UK, June 12 - 17, pages 52–61, 2016.
66
John McCarthy.
Situations, actions and causal laws.
Technical Report Technical Report Memo 2, Stanford University Artificial Intelligence Laboratory, Stanford, CA, 1963.
Reprinted in Marvin Minsky, editor, Semantic Information Processing, MIT Press, 1968.
67
John McCarthy and Patrick Hayes.
Some Philosophical Problems from the Standpoint of Artificial Intelligence.
In B. Meltzer and D. Michie, editors, Machine Intelligence, volume 4, pages 463–502. Edinburgh Univ. Press, 1969.
68
Drew V. McDermott.
Using regression-match graphs to control search in planning.
Artif. Intell., 109(1-2):111–159, 1999.
69
Sheila A. McIlraith.
Integrating Actions and State Constraints: A Closed-form Solution to the Ramification Problem (sometimes).
Artif. Intell., 116(1-2):87–121, 2000.
70
Rob Miller.
A case study in reasoning about actions and continuous change.
In W. Wahlster, editor, Proceedings of the 12th European Conference on Artificial Intelligence (ECAI'96), pages 624–628, 1996.
71
Anil Nerode and Wolf Kohn.
Models for hybrid systems: Automata, topologies, controllability, observability.
In Robert L. Grossman, Anil Nerode, Anders P. Ravn, and Hans Rischel, editors, Hybrid Systems, volume 736 of Lecture Notes in Computer Science, pages 317–356. Springer, 1992.
72
Isaac Newton.
"The Principia: Mathematical Principles of Natural Philosophy", a new translation by I. Bernard Cohen and Anne Whitman.
University of California Press, 1999.
73
Edwin P. D. Pednault.
ADL and the state-transition model of action.
J. Log. Comput., 4(5):467–512, 1994.
74
Chiara Piacentini, Daniele Magazzeni, Derek Long, Maria Fox, and Chris J. Dent.
Solving realistic unit commitment problems using temporal planning: Challenges and solutions.
In Amanda Jane Coles, Andrew Coles, Stefan Edelkamp, Daniele Magazzeni, and Scott Sanner, editors, Proceedings of the Twenty-Sixth International Conference on Automated Planning and Scheduling, ICAPS 2016, London, UK, June 12-17, 2016, pages 421–430. AAAI Press, 2016.
75
Javier Pinto and Raymond Reiter.
Reasoning about time in the situation calculus.
Ann. Math. Artif. Intell., 14(2-4):251–268, 1995.
76
Wiktor Mateusz Piotrowski, Maria Fox, Derek Long, Daniele Magazzeni, and Fabio Mercorio.
Heuristic planning for PDDL+ domains.
In Subbarao Kambhampati, editor, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, pages 3213–3219. IJCAI/AAAI Press, 2016.
77
Fiora Pirri, Geoffrey E. Hinton, and Hector J. Levesque.
In Memory of Ray Reiter (1939-2002) .
AI Mag., 23(4):93, 2002.
78
Fiora Pirri and Ray Reiter.
Some contributions to the metatheory of the situation calculus.
Journal of the ACM (JACM), 46(3):325–361, 1999.
79
Fiora Pirri and Raymond Reiter.
Planning with natural actions in the situation calculus.
In Jack Minker, editor, Logic-Based Artificial Intelligence, page 213–231. Kluwer, 2000.
ISBN 9780792372240.
80
David Poole.
Decision theory, the situation calculus and conditional plans, available at https://www.cs.ubc.ca/~poole/abstracts/sitc.html.
Electron. Trans. Artif. Intell., 2:105–158, 1998.
81
Hadi Qovaizi.
Efficient Lifted Planning with Regression-Based Heuristics, Master Thesis.
Technical report, Ryerson University, Department of Copmputer Science, Sep 2019.
82
Bertram Raphael.
The Thinking Computer: Mind Inside Matter.
W.H. Freeman and Company, 1976.
83
Raymond Reiter.
The frame problem in the situation calculus: A simple solution (sometimes) and a completeness result for goal regression.
In V. Lifschitz, editor, Artificial Intelligence and Mathematical Theory of Computation: Papers in Honor of John McCarthy, pages 359–380, San Diego, 1991. Academic Press.
84
Raymond Reiter.
Proving properties of states in the situation calculus.
Artif. Intell., 64(2):337–351, 1993.
85
Raymond Reiter.
Natural actions, concurrency and continuous time in the situation calculus.
In Luigia Carlucci Aiello, Jon Doyle, and Stuart C. Shapiro, editors, Proceedings of the Fifth International Conference on Principles of Knowledge Representation and Reasoning (KR'96), Cambridge, Massachusetts, USA, November 5-8, 1996., pages 2–13. Morgan Kaufmann, 1996.
86
Raymond Reiter.
Knowledge in Action. Logical Foundations for Specifying and Implementing Dynamical Systems.
MIT, available at http://cognet.mit.edu/book/knowledge-action, 2001.
87
Enrico Scala, Patrik Haslum, Daniele Magazzeni, and Sylvie Thiébaux.
Landmarks for numeric planning problems.
In Carles Sierra, editor, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, pages 4384–4390. ijcai.org, 2017.
88
Enrico Scala, Patrik Haslum, and Sylvie Thiébaux.
Heuristics for numeric planning via subgoaling.
In Subbarao Kambhampati, editor, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, pages 3228–3234. IJCAI/AAAI Press, 2016.
89
Richard B. Scherl and Hector J. Levesque.
Knowledge, action, and the frame problem.
Artificial Intelligence, 144(1-2):1–39, 2003.
90
Mikhail Soutchanski.
An on-line decision-theoretic golog interpreter.
In Bernhard Nebel, editor, Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI 2001, Seattle, Washington, USA, August 4-10, 2001, pages 19–26. Morgan Kaufmann, 2001.
91
Steven H. Strogatz.
Infinite Powers: How Calculus Reveals the Secrets of the Universe.
Houghton Mifflin Harcourt, 2019.
92
Eugenia Ternovskaia.
Interval situation calculus.
In Proceedings of the ECAI-94 Workshop W5 on Logic and Change, pages 153–164, Amsterdam, Netherlands, 1994.
93
Marc Toussaint.
Logic-geometric programming: An optimization-based approach to combined task and motion planning.
In Qiang Yang and Michael J. Wooldridge, editors, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pages 1930–1936. AAAI Press, 2015.
94
Mauro Vallati, Daniele Magazzeni, Bart De Schutter, Lukás Chrpa, and Thomas Leo McCluskey.
Efficient macroscopic urban traffic models for reducing congestion: A PDDL+ planning approach.
In AAAI, pages 3188–3194, 2016.
95
Stavros Vassos and Hector J. Levesque.
How to progress a database III.
Artif. Intell., 195:203–221, 2013.
96
Stavros Vassos and Fabio Patrizi.
A classification of first-order progressable action theories in situation calculus.
In Francesca Rossi, editor, IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9, 2013, pages 1132–1138. IJCAI/AAAI, 2013.
97
R. Waldinger.
Achieving several goals simultaneously.
In E. Elcock and D. Michie, editors, Machine Intelligence, volume 8, pages 94–136, Edinburgh, Scotland, 1977. Ellis Horwood.
98
Daniel S. Weld.
Recent advances in AI planning.
AI Mag., 20(2):93–123, 1999.
99
Julia Wichlacz, Daniel Höller, and Jörg Hoffmann.
Landmark heuristics for lifted planning - extended abstract.
In Hang Ma and Ivan Serina, editors, Proceedings of the Fourteenth International Symposium on Combinatorial Search, SOCS 2021, Virtual Conference [Jinan, China], July 26-30, 2021, pages 242–244. AAAI Press, 2021.


Bibliography with Web links.


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Hybrid Temporal Situation Calculus for Planning with Continuous Processes

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