Computational evaluations are crucial in modern problem-solving when we surpass theoretical algorithms or bounds. These experiments frequently take much work, and the sheer amount of needed resources makes it impossible to execute them on a single personal computer or laptop. Cluster schedulers allow for automatizing these tasks and scale to many computers. But, when we evaluate implementations of combinatorial algorithms, we depend on stable runtime results. Common approaches either limit parallelism or suffer from instable runtime measurements due to interference among jobs on modern hardware. The former is inefficient and not sustainable. The latter results in unreplicable experiments.
In this work, we address this issue and offer an acceptable balance between efficiency, software, hardware complexity, reliability, and replicability. We investigate effects towards replicability stability and illustrate how to efficiently use widely employed cluster resources for parallel evaluations. Furthermore, we present solutions which mitigate issues that emerge from the concurrent execution of benchmark jobs. Our experimental evaluation shows that – despite parallel execution – our approach reduces the runtime instability on the majority of instances to one second.

Parallel Empirical Evaluations: Resilience despite Concurrency

Answer Set Programming (ASP) is a prominent modeling and solving framework. An inconsistent core (IC) of an ASP program is a subset of rules that is inconsistent. In the case of inconsistent programs, a smallest or subset-minimal IC yields crucial rules for the inconsistency. In this work, we study finding minimal ICs of ASP programs and key fragments from a complexity-theoretic perspective. Interestingly, due to the non-monotonic behavior of ASP, also consistent programs admit ICs. It turns out that there is an entire landscape of problems involving ICs with a diverse range of complexities up to the fourth level of the Polynomial Hierarchy. Deciding the existence of an IC is, already, for tight programs on the second level of the Polynomial Hierarchy. Furthermore, we give encodings for problems on the fragment of tight programs and illustrate their feasibility on a small set of instances.

Inconsistent Cores for ASP: The Perks and Perils of Non-Monotonicity

Answer Set Programming (ASP) is a framework in artificial intelligence and knowledge representation for declarative modeling and problem solving. Modern ASP solvers focus on the computation or enumeration of answer sets. However, a variety of probabilistic applications in reasoning or logic programming require counting answer sets. While counting can be done by enumeration, simple enumeration becomes immediately infeasible if the number of solutions is high. On the other hand, approaches to exact counting are of high worst-case complexity. In fact, in propositional model counting, exact counting becomes impractical. In this work, we present a scalable approach to approximate counting for answer set programming. Our approach is based on systematically adding XOR constraints to ASP programs, which divide the search space. We prove that adding random XOR constraints partitions the answer sets of an ASP program. In practice, we use a Gaussian elimination-based approach by lifting ideas from SAT to ASP and integrating it into a state of the art ASP solver, which we call ApproxASP. Finally, our experimental evaluation shows the scalability of our approach over the existing ASP systems.

ApproxASP - A Scalable Approximate Answer Set Counter

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. In this paper, 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.

Rushing and Strolling among Answer Sets - Navigation Made Easy

Description logics (DLs) are knowledge representation languages that are used in the field of artificial intelligence (AI).
A common technique is to query DL knowledge bases, e.g., by Boolean Datalog queries, and ask for entailment. 
But real world knowledge-bases are often obtained by combining data from various sources. 
This, inherently, might result in certain inconsistencies (with respect to a given query) and requires to estimate a degree of inconsistency before using a knowledge-base.
In this paper, we provide a complexity analysis of fixed-domain non-entailment (NE) on Datalog programs for well-established families of knowledge bases (KBs).
We exhibit a detailed complexity map for the decision cases, counting and projected counting, which may serve as a quantitative measure for inconsistency of a KB with respect to a query.
Our results show that NE is natural for the second, third, and fourth level of the polynomial (counting) hierarchy depending on the type of the studied query (stratified, normal, disjunctive) and one level higher for the projected versions.
Further, we show fixed-parameter tractability by bounding the treewidth, provide a constructive algorithm, and show its theoretical limitation in terms of conditional lower bounds.

Knowledge-Base Degrees of Inconsistency: Complexity and Counting

Main Track - CSO

technical paper

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

KRR: Logic Programming

poster

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Main_Track

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

Johannes Klaus Fichte

5

Presentations

Parallel Empirical Evaluations: Resilience despite Concurrency

Inconsistent Cores for ASP: The Perks and Perils of Non-Monotonicity

ApproxASP - A Scalable Approximate Answer Set Counter

Rushing and Strolling among Answer Sets - Navigation Made Easy

Knowledge-Base Degrees of Inconsistency: Complexity and Counting

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