| 1. |
- Calvanese, Diego, et al.
(författare)
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Verification and Monitoring for First-Order LTL with Persistence-Preserving Quantification over Finite and Infinite Traces
- 2022
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Ingår i: IJCAI International Joint Conference on Artificial Intelligence. - California : International Joint Conferences on Artificial Intelligence. - 9781956792003 ; , s. 2553-2560
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Konferensbidrag (refereegranskat)abstract
- We address the problem of model checking first-order dynamic systems where new objects can be injected in the active domain during execution. Notable examples are systems induced by a first-order action theory expressed, e.g., in the situation calculus. Recent results show that, under state-boundedness, such systems, in spite of having a first-order representation of the state, admit decidable model checking for full first-order mu-calculus. However, interestingly, model checking remains undecidable in the case of first-order LTL (LTL-FO). In this paper, we show that in LTL-FOp, the fragment of LTL-FO where quantification ranges only over objects that persist along traces, model checking state-bounded systems becomes decidable over infinite and finite traces. We then employ this result to show how to handle monitoring of LTL-FOp properties against a trace stemming from an unknown state-bounded dynamic system, simultaneously considering the finite trace up to the current point, and all its possibly infinite future continuations.
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| 2. |
- Dechter, R., et al.
(författare)
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Preface
- 2022
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Ingår i: IJCAI International Joint Conference on Artificial Intelligence. - : AAAI Press. - 1045-0823. - 9781956792003 ; , s. lvi-lvii
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 3. |
- Dechter, R., et al.
(författare)
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WELCOME TO IJCAI-ECAI 2022
- 2022
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Ingår i: IJCAI International Joint Conference on Artificial Intelligence. - : AAAI Press. - 1045-0823. - 9781956792003
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 4. |
- Eriksson, Leif, 1993-, et al.
(författare)
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A Multivariate Complexity Analysis of Qualitative Reasoning Problems
- 2022
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Ingår i: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-22). - California : International Joint Conferences on Artificial Intelligence. - 9781956792003 ; , s. 1804-1810
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Konferensbidrag (refereegranskat)abstract
- Qualitative reasoning is an important subfield of artificial intelligence where one describes relationships with qualitative, rather than numerical, relations. Many such reasoning tasks, e.g., Allen's interval algebra, can be solved in 2^O(n*log n) time, but single-exponential running times 2^O(n) are currently far out of reach. In this paper we consider single-exponential algorithms via a multivariate analysis consisting of a fine-grained parameter n (e.g., the number of variables) and a coarse-grained parameter k expected to be relatively small. We introduce the classes FPE and XE of problems solvable in f(k)*2^O(n), respectively f(k)^n, time, and prove several fundamental properties of these classes. We proceed by studying temporal reasoning problems and (1) show that the partially ordered time problem of effective width k is solvable in 16^{kn} time and is thus included in XE, and (2) that the network consistency problem for Allen's interval algebra with no interval overlapping with more than k others is solvable in (2nk)^{2k}*2^n time and is included in FPE. Our multivariate approach is in no way limited to these to specific problems and may be a generally useful approach for obtaining single-exponential algorithms.
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| 5. |
- Ferber, Patrick, et al.
(författare)
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Learning and Exploiting Progress States in Greedy Best-First Search
- 2022
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Ingår i: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI 2022). - California : International Joint Conferences on Artificial Intelligence. - 9781956792003 ; , s. 4740-4746
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Konferensbidrag (refereegranskat)abstract
- Previous work introduced the concept of progress states. After expanding a progress state, a greedy best-first search (GBFS) will only expand states with lower heuristic values. Current methods can identify progress states only for a single task and only after a solution for the task has been found. We introduce a novel approach that learns a description logic formula characterizing all progress states in a classical planning domain. Using the learned formulas in a GBFS to break ties in favor of progress states often significantly reduces the search effort.
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| 6. |
- Righetti, Guendalina, et al.
(författare)
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Asymmetric Hybrids : Dialogues for Computational Concept Combination (Extended Abstract)
- 2022
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Ingår i: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22). - California : International Joint Conferences on Artificial Intelligence Organization. - 9781956792003 ; , s. 5329-5333
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Konferensbidrag (refereegranskat)abstract
- When considering two concepts in terms of extensional logic, their combination will often be trivial, returning an empty extension. Consider e.g. “a Fish Vehicle”, i.e., “a Vehicle which is also a Fish”. Still, people use sophisticated strategies to produce new, non-empty concepts. All these strategies involve the human ability to mend the conflicting attributes of the input concepts and to create new properties of the combination. We focus in particular on the case where a Head concept has superior ‘asymmetric’ control over steering the resulting combination (or hybridisation) with a Modifier concept. Specifically, we propose a dialogical model of the cognitive and logical mechanics of this asymmetric form of hybridisation. Its implementation is then evaluated using a combination of example ontologies.
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| 7. |
- Winikoff, Michael, et al.
(författare)
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Why Bad Coffee? Explaining BDI Agent Behaviour with Valuings (Extended Abstract)
- 2022
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Ingår i: IJCAI International Joint Conference on Artificial Intelligence. - Palo Alto, CA : AAAI Press. - 9781956792003 ; , s. 5782-5786
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Konferensbidrag (refereegranskat)abstract
- An important issue in deploying an autonomous system is how to enable human users and stakeholders to develop an appropriate level of trust in the system. It has been argued that a crucial mechanism to enable appropriate trust is the ability of a system to explain its behaviour. Obviously, such explanations need to be comprehensible to humans. Due to the perceived similarity in functioning between humans and autonomous systems, we argue that it makes sense to build on the results of extensive research in social sciences that explores how humans explain their behaviour. Using similar concepts for explanation is argued to help with comprehensibility, since the concepts are familiar. Following work in the social sciences, we propose the use of a folk-psychological model that utilises beliefs, desires, and “valuings”. We propose a formal framework for constructing explanations of the behaviour of an autonomous system, present an (implemented) algorithm for giving explanations, and present evaluation results. © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.
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