Answer Set Research Articles

Problem Decomposition and Multi-shot ASP Solving for Job-shop Scheduling

AbstractScheduling methods are important for effective production and logistics management, where tasks need to be allocated and performed with limited resources. In particular, the Job-shop Scheduling Problem (JSP) is a well known and challenging combinatorial optimization problem in which tasks sharing a machine are to be arranged in a sequence such that encompassing jobs can be completed as early as possible. Given that already moderately sized JSP instances can be highly combinatorial, and neither optimal schedules nor the runtime to termination of complete optimization methods is known, efficient approaches to approximate good-quality schedules are of interest. In this paper, we propose problem decomposition into time windows whose operations can be successively scheduled and optimized by means of multi-shot Answer Set Programming (ASP) solving. From a computational perspective, decomposition aims to split highly complex scheduling tasks into better manageable subproblems with a balanced number of operations so that good-quality or even optimal partial solutions can be reliably found in a small fraction of runtime. Regarding the feasibility and quality of solutions, problem decomposition must respect the precedence of operations within their jobs and partial schedules optimized by time windows should yield better global solutions than obtainable in similar runtime on the entire instance. We devise and investigate a variety of decomposition strategies in terms of the number and size of time windows as well as heuristics for choosing their operations. Moreover, we incorporate time window overlapping and compression techniques into the iterative scheduling process to counteract window-wise optimization limitations restricted to partial schedules. Our experiments on JSP benchmark sets of several sizes show that successive optimization by multi-shot ASP solving leads to substantially better schedules within the runtime limit than global optimization on the full problem, where the gap increases with the number of operations to schedule. While the obtained solution quality still remains behind a state-of-the-art Constraint Programming system, our multi-shot solving approach comes closer the larger the instance size, demonstrating good scalability by problem decomposition.

An ASP Approach for Reasoning on Neural Networks under a Finitely Many-Valued Semantics for Weighted Conditional Knowledge Bases

AbstractWeighted knowledge bases for description logics with typicality have been recently considered under a “concept-wise” multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logical semantics of multilayer perceptrons (MLPs). In this paper we consider weighted conditional $\mathcal{ALC}$ knowledge bases with typicality in the finitely many-valued case, through three different semantic constructions. For the boolean fragment $\mathcal{LC}$ of $\mathcal{ALC}$ we exploit answer set programming and asprin for reasoning with the concept-wise multipreference entailment under a $\varphi$ -coherent semantics, suitable to characterize the stationary states of MLPs. As a proof of concept, we experiment the proposed approach for checking properties of trained MLPs.

Efficient Lifting of Symmetry Breaking Constraints for Complex Combinatorial Problems

AbstractMany industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of representative and easily solvable instances is available, which is often not the case in practical applications. This work extends the learning framework and implementation of a model-based approach for Answer Set Programming to overcome these limitations and address challenging problems, such as the Partner Units Problem. In particular, we incorporate a new conflict analysis algorithm in the Inductive Logic Programming system ILASP, redefine the learning task, and suggest a new example generation method to scale up the approach. The experiments conducted for different kinds of Partner Units Problem instances demonstrate the applicability of our approach and the computational benefits due to the first-order constraints learned.

Rushing and Strolling among Answer Sets – Navigation Made Easy

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.

ApproxASP – a Scalable Approximate Answer Set Counter

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.

Axiomatization of Aggregates in Answer Set Programming

The paper presents a characterization of logic programs with aggregates based on many-sorted generalization of operator SM that refers neither to grounding nor to fixpoints. This characterization introduces new symbols for aggregate operations and aggregate elements, whose meaning is fixed by adding appropriate axioms to the result of the SM transformation. We prove that for programs without positive recursion through aggregates our semantics coincides with the semantics of the answer set solver Clingo.

A Calculus for Computing Structured Justifications for Election Outcomes

In the context of social choice theory, we develop a tableau-based calculus for reasoning about voting rules. This calculus can be used to obtain structured explanations for why a given set of axioms justifies a given election outcome for a given profile of voter preferences. We then show how to operationalise this calculus, using a combination of SAT solving and answer set programming, to arrive at a flexible framework for presenting human-readable justifications to users.

Compilation of Aggregates in ASP Systems

Answer Set Programming (ASP) is a well-known declarative AI formalism for knowledge representation and reasoning. State-of-the-art ASP implementations employ the ground&solve approach, and they were successfully applied to industrial and academic problems. Nonetheless there are classes of ASP programs whose evaluation is not efficient (sometimes not feasible) due to the combinatorial blow-up of the program produced by the grounding step. Recent researches suggest that compilation-based techniques can mitigate the grounding bottleneck problem. However, no compilation-based technique has been developed for ASP programs that contain aggregates, which are one of the most relevant and commonly-employed constructs of ASP. In this paper, we propose a compilation-based approach for ASP programs with aggregates. We implement it on top of a state-of-the-art ASP system, and evaluate the performance on publicly-available benchmarks. Experiments show our approach is effective on ground-intensive ASP programs.

ASP-Based Declarative Process Mining

We put forward Answer Set Programming (ASP) as a solution approach for three classical problems in Declarative Process Mining: Log Generation, Query Checking, and Conformance Checking. These problems correspond to different ways of analyzing business processes under execution, starting from sequences of recorded events, a.k.a. event logs. We tackle them in their data-aware variant, i.e., by considering events that carry a payload (set of attribute-value pairs), in addition to the performed activity, specifying processes declaratively with an extension of linear-time temporal logic over finite traces (LTLf). The data-aware setting is significantly more challenging than the control-flow one: Query Checking is still open, while the existing approaches for the other two problems do not scale well. The contributions of the work include an ASP encoding schema for the three problems, their solution, and experiments showing the feasibility of the approach.

Linearity

In January 2021, Saudi Crown Prince Mohammed bin Salman Al Saud launched The Line city project. Even for the Saudis’ fantastical ambitions, the project looks amazing: a city stretched along a straight line for 170 kilometers across the sandy desert from the Red Sea coast to the capital of the mountainous Tabuk province. The city will have three floors: a pedestrian zone on the surface, service infrastructure on the underground floor, and on the floor below there will be highspeed railways. It seems ironic that the ideas of a linear city, which were actively developed in the USSR, find large-scale implementation in a country with an absolute monarchy. Why do the ideas of a linear city come to life in our present-day world? How are these ideas transformed by digital technologies? How is the very notion of linearity changing as the fabric of the city becomes increasingly virtual and roads are replaced by 5G channels? As usual, we do not claim to have a comprehensive set of answers to these questions related to the cutting edge of urban theory and philosophy of the city. But is it just the city? Giant projects of the revival of the Great Silk Road, the concept of the Eastern pivot and Greater Eurasia form the image of a linear continent – not a city, but a giant complex of many cities in a number of countries. A group of architects from sub-Saharan Africa is initiating the Great Green Wall competition: a great band of green oases is to block the hot breath of the desert and mitigate the climate of the entire equatorial Africa. Today linearity looks like a global challenge and at the same time a strict condition for development on the most diverse levels: neighbourhood, city, region, continent, and humanity.

Strong Equivalence of Logic Programs with Counting

AbstractIn answer set programming, two groups of rules are considered strongly equivalent if they have the same meaning in any context. In some cases, strong equivalence of programs in the input language of the grounder gringo can be established by deriving rules of each program from rules of the other. The possibility of such proofs has been demonstrated for a subset of that language that includes comparisons, arithmetic operations, and simple choice rules, but not aggregates. This method is extended here to a class of programs in which some uses of the #count aggregate are allowed.

Improving Symbolic System-Level Synthesis by Solver Coordination and Domain-Specific Heuristics

Deciding binding, routing, and scheduling within system synthesis for hard real-time systems can be a challenging task. Symbolic methods leveraging results from the area of satisfiability modulo theories (SMT) solving have shown to be scalable methods for this by splitting the work between a logic solver for routing and binding, and a background theory solver performing schedulability analysis. For these methods, in order to prune the search space of infeasible implementations efficiently, a feedback by the background theory is required. It can be observed that previous approaches might fail here as feedback cannot be derived within a reasonable amount of time. We propose a coordinated synthesis approach that overcomes this issue. Here, we leverage an answer set solver as logic solver that is enhanced with a scheduling-aware binding and routing refinement. Based on the answer set solver’s decisions for binding and routing, a background theory solver then computes time-triggered schedules to resolve resource access conflicts. If no feasible schedule exists, a feedback to the answer set solver can be derived within reasonable time. Our experiments synthesizing massively parallel hardware architectures show that our approach increases the applicability of symbolic synthesis considerably. While more than half of the investigated instances in our experiments cannot be solved in the non-coordinated approach already at small 2-dimensional 3×3 tiled mesh hardware architectures with 60% average computational utilization per tile, the coordinated synthesis approach scales up to 5×5 architectures with average utilization of 70% per tile (2.8× the hardware architecture size than before). Furthermore, we increase the scalability and the robustness of our approach by encoding our domain-knowledge within domain-specific heuristics in our designated answer set solver. Within our experiments, we observe that the domain-specific heuristics enable us to scale up to 6×6 architectures with 70% average utilization per tile.

Social Determinants in Self-Protective Behavior Related to COVID-19: Association Rule-Mining Study.

BackgroundHuman behavior is crucial in health outcomes. Particularly, individual behavior is a determinant of the success of measures to overcome critical conditions, such as a pandemic. In addition to intrinsic public health challenges associated with COVID-19, in many countries, some individuals decided not to get vaccinated, streets were crowded, parties were happening, and businesses struggling to survive were partially open, despite lockdown or stay-at-home instructions. These behaviors contrast with the instructions for potential benefits associated with social distancing, use of masks, and vaccination to manage collective and individual risks.ObjectiveConsidering that human behavior is a result of individuals' social and economic conditions, we investigated the social and working characteristics associated with reports of appropriate protective behavior in Brazil.MethodsWe analyzed data from a large web survey of individuals reporting their behavior during the pandemic. We selected 3 common self-care measures: use of protective masks, distancing by at least 1 m when out of the house, and handwashing or use of alcohol, combined with assessment of the social context of respondents. We measured the frequency of the use of these self-protective measures. Using a frequent pattern–mining perspective, we generated association rules from a set of answers to questions that co-occur with at least a given frequency, identifying the pattern of characteristics of the groups divided according to protective behavior reports.ResultsThe rationale was to identify a pool of working and social characteristics that might have better adhesion to behaviors and self-care measures, showing these are more socially determined than previously thought. We identified common patterns of socioeconomic and working determinants of compliance with protective self-care measures. Data mining showed that social determinants might be important to shape behavior in different stages of the pandemic.ConclusionsIdentification of context determinants might be helpful to identify unexpected facilitators and constraints to fully follow public policies. The context of diseases contributes to psychological and physical health outcomes, and context understanding might change the approach to a disease. Hidden social determinants might change protective behavior, and social determinants of protective behavior related to COVID-19 are related to work and economic conditions.Trial RegistrationNot applicable.

Comparing the Reasoning Capabilities of Equilibrium Theories and Answer Set Programs

Answer Set Programming (ASP) is a well established logical approach in artificial intelligence that is widely used for knowledge representation and problem solving. Equilibrium logic extends answer set semantics to more general classes of programs and theories. When intertheory relations are studied in ASP, or in the more general form of equilibrium logic, they are usually understood in the form of comparisons of the answer sets or equilibrium models of theories or programs. This is the case for strong and uniform equivalence and their relativised and projective versions. However, there are many potential areas of application of ASP for which query answering is relevant and a comparison of programs in terms of what can be inferred from them may be important. We formulate and study some natural equivalence and entailment concepts for programs and theories that are couched in terms of inference and query answering. We show that, for the most part, these new intertheory relations coincide with their model-theoretic counterparts. We also extend some previous results on projective entailment for theories and for the new connective called fork.

Professional Help-Seeking in Men Experiencing Sexual Problems – The Role of Sexual Identity and Minority Stress

Only a minority of men experiencing sexual problems will seek professional help and the proportion of gay or bisexual men can be even lower. To investigate if sexual identity and minority stress are related to professional help-seeking in Polish gay and bisexual men. Sexual identity was measured with the standard question: "Do you consider yourself to be heterosexual, gay, or bisexual?" Men who provided a complete set of answers to the study tools and replied "yes" to the question: "Have you ever had a problem with sexual functioning that lasted at least several months?" were included (Ntotal =644, Nstraight = 203, Ngay = 324, Nbi = 117). A simple question on help-seeking was: "Did you seek professional assistance then?" Other data were gathered with the use of a self-constructed questionnaire. Minority stress processes, that is, internalized homophobia, expectations of rejection, and identity concealment, were measured with the subscales of the Sexual Minority Stress Scale.Bivariate analyses and multivariate logistic regressions were performed to test the statistical significance of sexual identity and minority stress processes as predictors of professional help-seeking. Contacting a specialist when experiencing a sexual problem. A total of 84.5% of all men did not seek professional help. Gay identity (OR = 0.58, P = .045), as opposed to bisexual identity, was significantly related to reduced odds of consulting a specialist. Age (OR = 1.03, P = .005), number of doctor's visits per year (OR = 1.51, P < .001), and a psychiatric diagnosis (OR = 1.65, P = .043) were positively related to help-seeking behaviors. Identity concealment significantly decreased the likelihood of consulting a specialist (OR = 0.94, P = .017). Specialists need to be aware that gay identity and identity concealment may prevent a proportion of men from seeking their help and thus should be publicly explicit about their inclusive and nonpathologizing approach to sexual diversity. The major strengths of the study include the use of a relatively large sample size and data from the little recognized Polish context characterized by a predominantly hostile anti-LGBT social climate, and exploration of a neglected topic of substantial significance at the public and individual levels. The major limitations are the use of nonprobability sampling, cross-sectional self-report design, and a single question to capture the presence of sexual problems with no measurement of associated distress. Gay men are at risk of avoiding help-seeking when experiencing sexual problems because of identity concealment.

Benchmarking Answer Set Programming systems for resource allocation in business processes

Declarative logic programming formalisms are well-suited to model various optimization and configuration problems. In particular, Answer Set Programming (ASP) systems have gained popularity, for example, to deal with scheduling problems present in several domains. The main goal of this paper is to devise a benchmark for ASP systems to assess their performance when dealing with complex and realistic resource allocation with objective optimization. To this end, we provide (i) a declarative and compact encoding of the resource allocation problem in ASP (compliant with the ASP Core-2 standard), (ii) a configurable ASP systems benchmark named BRANCH that is equipped with resource allocation instance generators that produce problem instances of different sizes with adjustable parameters (e.g., in terms of process complexity, organizational and temporal constraints), and (iii) an evaluation of four state-of-the-art ASP systems using BRANCH. This solid application-oriented benchmark serves the ASP community with a tool that leads to potential optimizations and improvements in encodings and further drives the development of ASP solvers. On the other hand, resource allocation is an important problem that still lacks adequate automated tool support in the context of Business Process Management (BPM). The ASP problem encoding, ready-to-use ASP systems and problem instance generators benefit the BPM community to tackle the problem at scale and mitigate the lack of openly available problem instance data.

Using default logic for agent behavior testing

An agent is an autonomous entity that can perform actions to achieve its goals. It acts in a dynamic environment that may engender failures regarding its behavior. Therefore, a formal testing/verification approach of the agent is required to ensure the correctness of its behavior. In this paper, we propose a Default Logic formalism to abstract an agent behavior as knowledge and reasoning rules, and to verify and test the consistency of the behavior. The considered agents are implemented with JADE framework. Also, agent abstraction is translated into Answer Set Programming and solved by Clingo to generate dynamic and adaptive test cases of the agent behavior. The dynamic test cases allow predicting the agent behavior when a new information arises in the system.

Quantifying means-end reasoning skills in simulation-based training: a logic-based approach

We develop a logic-based approach for designing simulation-based training scenarios. Our methodology embodies a concise definition of the scenario concept and integrates the notions of training goals, acceptable versus unacceptable actions and performance scoring. The approach applies classical artificial intelligence (AI) planning to extract coherent plays from a causal description of the training domain. The domain- and task-specific parts are defined in a high-level action description language [Formula: see text]. Generic causal and temporal logic is added when the causal theory is compiled into the underlying Answer Set Programming (ASP) language. The ASP representation is used to derive a scoring function that reflects the quality of a play or training session, based on a distinction of states and actions into green (acceptable) and red (unacceptable) ones. To that end, we add to the casual theory a set of norms that specify an initial assignment of colors. The ASP engine uses these norms as axioms and propagates colors by consulting the causal theory. We prove that any set of such norms constitutes a conservative extension of the underlying causal theory. With this work, we hope to lay the foundation for the development of design and analysis tools for exercise managers. We envision a software system that lets an exercise manager view all plays of a tentative scenario design, with expediency information and scores for each possible play. Our approach is applicable to any domain in which means-ends reasoning is pertinent. We illustrate the approach in the domain of crisis response and management.

Accurate and prompt answering framework based on customer reviews and question-answer pairs

As e-commerce markets have gradually expanded, online shopping malls have provided various services aiming to secure competitiveness. A service for providing an accurate and prompt response when a customer writes an inquiry regarding a product represents a space directly connected to the customer and plays an important role, as it is directly related to product sales. However, the current online shopping mall answering service has disadvantages, e.g., it takes time for an administrator to write an answer directly, or to provide an answer within a set of answers. In this paper, we propose an answer framework for solving this problem, based on customer reviews. When a user writes a query, the framework provides an appropriate answer in real time through the system’s question-and-answer pairs and customer reviews. The framework’s performance is verified through a qualitative evaluation. In addition, it is confirmed that a customized model for reflecting the characteristics of each shopping mall can be created by using additional information from the collected data. The proposed framework is expected to support customers’ online shopping through more reliable and efficient information retrieval, and to reduce shopping mall operation and maintenance costs.

A Practical Approach to Assessing the Impact of Citizen Science towards the Sustainable Development Goals

The United Nations’ Sustainable Development Goals (SDGs) are an important global framework which provides a shared vision for a more sustainable future for all people and the planet. In the last five years, citizen science as a discipline has paid increasing attention to the SDGs and the contributions that citizen science could make towards their achievement. This paper presents a collection of 51 questions and a corresponding set of answers which can be used by citizen science projects to self-assess their impact towards the SDGs. The questions and answers were originally derived from the official UN description of the SDG targets and indicators and were refined through a workshop and series of nine interviews with citizen science project coordinators. The outcomes of the workshop and interviews reveal the challenges of assessing impacts towards the SDGs in a way which is relevant to the majority of citizen science projects. In many cases, the wording of the SDGs had to be altered to make sense in the context of citizen science. The final set of questions and answers are structured to reflect two pathways of impact: citizen science contributing to the (official) monitoring of the SDGs, and citizen science contributing to the direct achievement of the SDGs.