Sarah Alice Gaggl/NAVAS Workshop-Wien: Unterschied zwischen den Versionen
Dominik Rusovac (Diskussion | Beiträge) KKeine Bearbeitungszusammenfassung |
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Answer set programming (ASP) is a popular declarative programming paradigm | Answer set programming (ASP) is a popular declarative programming paradigm with a wide range | ||
with a wide range of applications in artificial intelligence. | of applications in artificial intelligence. Oftentimes, when modeling an AI problem with ASP, and in | ||
when modeling an AI problem with ASP, and in particular when we are interested | particular when we are interested beyond simple search for optimal solutions, an actual solution, differences between solutions, or number of solutions of the ASP program matter. For example, when | ||
beyond simple search for optimal solutions, an actual solution, differences | a user aims to identify a specific answer set according to her needs, or requires the total number | ||
between solutions, or number of solutions of the ASP program matter. | of diverging solutions to comprehend probabilistic applications such as reasoning in medical domains. Then, there are only certain problem specific and handcrafted encoding techniques available | ||
example, when a user aims to identify a specific answer set according to her | to navigate the solution space of ASP programs, which is oftentimes not enough. We | ||
needs, or requires the total number of diverging solutions to comprehend | propose a formal and general framework for interactive navigation toward desired subsets of answer | ||
probabilistic applications such as reasoning in medical domains. | sets analogous to faceted browsing. Our approach enables the user to explore the solution space by | ||
are | consciously zooming in or out of sub-spaces of solutions at a certain configurable pace. We illustrate | ||
that weighted faceted navigation is computationally hard. Finally, we provide an implementation | |||
not enough. | of our approach that demonstrates the feasibility of our framework for incomprehensible solution spaces. | ||
interactive navigation toward desired subsets of answer sets analogous to | |||
faceted browsing. Our approach enables the user to explore the solution space | |||
configurable pace. We illustrate that weighted faceted navigation is | |||
computationally hard. Finally, we provide an implementation of our approach | |||
that demonstrates the feasibility of our framework for incomprehensible | |||
solution spaces. | |||
|Organization= | |Organization= | ||
Version vom 10. Mai 2022, 11:54 Uhr
The NAVAS workshop on Navigation Approaches for Answer Sets is jointly organized by research groups at TU Wien and TU Dresden will take place in Vienna , Austria, may 23-25, 2022.
The NaVAS workshop will take place from Monday, May 21th until Wednesday May 23th of 2022.
Monday, May 23th:
- 09:15 Welcome
- 09:20 Talk: NAVAS - Navigation in the solution space of answer sets and Visualization for Argument Frameworks - Sarah Alice Gaggl
- 09:45 Talk: Tunas - Fishing for Diverse Answer Sets: A Multi-Shot Trade up Strategy - Elisa Böhl
- 10:15 Coffee break
- 10:30 Talk: Flexible Dispute Derivations with Forward and Backward Arguments for Assumption-Based Argumentation - Martin Diller
- 11:30 Talk: Rushing and Strolling among Answer Sets - Navigation Made Easy Dominik Rusovac
Tuesday, May 24th:
- 14:00 - 19:00 CAIML Event - Edward A. Lee, Pedro Cabalar
For the schedule on on Monday, May 23th the following talks are presented.
Fishing for Diverse Answer Sets: A Multi-Shot Trade up Strategy
Answer set programming (ASP) solvers have advanced in the recent years, with a variety of different specialisation and overall development. Thus, even more complex and detailed programs can be solved. A side effect of this development are growing solution spaces and the problem of how to find those answer sets one is interested in. One general approach is to give an overview in form of a small number of highly diverse answer sets. By choosing a favourite and repeating the process the user is able to leap through the solution space. But finding highly diverse answer sets is computationally expensive. In this paper we introduce a new approach called Tunas for Trade Up Navigation for Answer Sets to find diverse answer sets by reworking existing solution collections. The core idea is to collect diverse answer sets. Once no more answer sets can be added to the collection, the program is allowed to trade answer sets from the collection for different answer sets, as long as the collection grows and stays diverse. Elaboration of the approach is possible in three variations, which we implemented and compared to established methods in an empirical evaluation. The evaluation shows that the Tunas approach is competitive with existing methods, and that efficiency of the approach is highly connected to the underlying logic program.
Flexible Dispute Derivations with Forward and Backward Arguments for Assumption-Based Argumentation
Assumption-based argumentation (ABA) is one of the main general frameworks for structured argumentation. Dispute derivations for ABA allow for evaluating claims in a dialectical manner: i.e. on the basis of an exchange of arguments and counter-arguments for a claim between a proponent and an opponent of the claim. Current versions of dispute derivations are geared towards determining (credulous) acceptance of claims w.r.t. the admissibility-based semantics that ABA inherits from abstract argumentation. Relatedly, they make use of backwards or top down reasoning for constructing arguments. In this work we define flexible dispute derivations with forward as well as backward reasoning allowing us, in particular, to also have dispute derivations for finding admissible, complete, and stable assumption sets rather than only determine acceptability of claims. We give an argumentation-based definition of such dispute derivations and a more implementation friendly alternative representation in which disputes involve exchange of claims and rules rather than arguments. These can be seen as elaborations on, in particular, existing graph-based dispute derivations on two fronts: first, in also allowing for forward reasoning; second, in that all arguments put forward in the dispute are represented by a graph and not only the proponents.
Answer set programming (ASP) is a popular declarative programming paradigm with a wide range of applications in artificial intelligence. Oftentimes, when modeling an AI problem with ASP, and in particular when we are interested beyond simple search for optimal solutions, an actual solution, differences between solutions, or number of solutions of the ASP program matter. For example, when a user aims to identify a specific answer set according to her needs, or requires the total number of diverging solutions to comprehend probabilistic applications such as reasoning in medical domains. Then, there are only certain problem specific and handcrafted encoding techniques available to navigate the solution space of ASP programs, which is oftentimes not enough. We propose a formal and general framework for interactive navigation toward desired subsets of answer sets analogous to faceted browsing. Our approach enables the user to explore the solution space by consciously zooming in or out of sub-spaces of solutions at a certain configurable pace. We illustrate that weighted faceted navigation is computationally hard. Finally, we provide an implementation
of our approach that demonstrates the feasibility of our framework for incomprehensible solution spaces.Organization
