Answer set programming unleashed!

Contribution of this work is to Define the Creative Composition Problem (CCP) for Human Well-being Optimization by Construction of Knowledge Graph using Knowledge Representation and logic-based Artificial Intelligence reasoning-planning where the computation of the Optimal Solution is achieved by Dynamic Programming or Logic Programming. The Creative Composition Problem is embedded within Cecilia: an architecture of a digital companion artificial intelligence agent system composer of dialogue scripts for Well-being and Mental Health. Where Cecilia Framework is instantiated in Well-being and Mental Health domain for optimal well-being development of first year university students. We define the ‘The Problem of Creating a Dialogue Composition (PCDC)’ and we propose a feasible and optimal solution of it. CCP is instantiated in this applied domain to solve PCDC optimizing the Mental Health and Well-being of the student. CCP as PCDC is applied to optimize maximizing the mental health of the student but also maximizing the smoothness, coherence, enjoyment and engagement each time the dialogue session is composed. Cecilia helps students to manage stress/anxiety to attempt the prevention of depression. Students can interact through the digital companion making questions and answers. While the system “learns” from the user it allows the user to learn from herself. Once the student discovers elements that were unnoticed by her, she will find a better way to improve when discovering her points of improvement.

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“…Conceptually the MAIC in Cecilia reasons using Answer Set Programming (ASP) [27,82] and consists of two modules described in Sect. 3.…”

Section: Creative Reasoning-planning: the Master-agent Artificial Intmentioning

confidence: 99%

Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution

Galindo

Moreno²

2021

Lecture Notes in Computer Science

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“…In this section, we recall the concepts and notions of Answer Set Programming (ASP) that we need for this article. We refer to [18,19,114] for more background and references. We start with syntax and semantics of ASP programs and then recall some notions that are useful for this work.…”

Section: Answer Set Programmingmentioning

confidence: 99%

Abstraction for Non-ground Answer Set Programs

Saribatur

Schüller

Eiter

2019

Logics in Artificial Intelligence

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ion is an important technique utilized by humans in model building and problem solving, in order to figure out key elements and relevant details of a world of interest. This naturally has led to investigations of using abstraction in AI and Computer Science to simplify problems, especially in the design of intelligent agents and automated problem solving. By omitting details, scenarios are reduced to ones that are easier to deal with and to understand, where further details are added back only when they matter. Despite the fact that abstraction is a powerful technique, it has not been considered much in the context of nonmonotonic knowledge representation and reasoning, and specifically not in Answer Set Programming (ASP), apart from some related simplification methods. In this work, we introduce a notion for abstracting from the domain of an ASP program such that the domain size shrinks while the set of answer sets (i.e., models) of the program is over-approximated. To achieve the latter, the program is transformed into an abstract program over the abstract domain while preserving the structure of the rules. We show in elaboration how this can be also achieved for single or multiple sub-domains (sorts) of a domain, and in case of structured domains like grid environments in which structure should be preserved. Furthermore, we introduce an abstraction-&-refinement methodology that makes it possible to start with an initial abstraction and to achieve automatically an abstraction with an associated abstract answer set that matches an answer set of the original program, provided that the program is satisfiable. Experiments based on prototypical implementations reveal the potential of the approach for problem analysis, by its ability to focus on the parts of the program that cause unsatisfiability and by achieving concrete abstract answer sets that merely reflect relevant details. This makes domain abstraction an interesting topic of research whose further use in important areas like Explainable AI remains to be explored.

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“…These characteristics offer significant advantages in knowledge representation, in terms of transparency, flexibility, and scalability (Zaniolo, 1991;Calimeri et al, 2018). Answer Set Programming (ASP) is among the most popular paradigms of declarative problem solving (Schaub and Woltran, 2018) with numerous applications in knowledge representation and reasoning (Erdem et al, 2016;Nogueira et al, 2001;Alviano et al, 2020;Brewka et al, 2011;Leone and Ricca, 2015).…”

Section: Introductionmentioning

confidence: 99%

Land Evaluation Configuration using Answer Set Programming

Karamesouti,

Tignon

2024

AGILE GIScience Ser.

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Abstract. In the realm of Land Evaluation (LE) interdisciplinary and transdisciplinary knowledge exchange is critical for land use preservation. Geographic Information Systems (GIS) are powerful tools for real-world Knowledge Representation (KR), facilitating inter- and transdisciplinary communication. In such knowledge exchange contexts, heterogeneity, ambiguity, abstraction are only indicative issues, underscoring the necessity for a rigorous commitment to broader transparency in KR. Answer Set Programming (ASP), a declarative, human-readable, logic-based formalism, could serve this objective and facilitate productive, case-relevant dialogues. Similarly to the fundamental GIS knowledge organization structures, ASP formalizes knowledge as entities and relations between them. In current work, leveraging Rossiter’s theoretical framework for LE, and employing ASP, we aim for greater transparency in the epistemological and ontological assumptions underpinning the complex LE problem. ASP-based system configuration is used to formalize the LE Problem Instance as Components (C) with Properties (P) and Values (V ). Fact-type specifications in predicate format materialize relations between problem components. Over 40 concepts, corresponding to distinct domains, 30 mereological relations and relational requirements between components, and 60 requirements on component properties have been described. We showcase the Problem Instance formalization of the non-spatial, single-area LE, based on Land Characteristics (LC), model type. The clear separation between domain knowledge (Problem Instance) and high-level theories (Problem Encoding) enables the consistent LE problem formalization using the ASP-based system configuration paradigm. A declarative Problem Instance formalization provides insight into the problem’s nature and assumptions. Modular knowledge formalization using ASP, among others, enhances flexibility, scalability, and adaptability, given new knowledge becomes available.

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Answer set programming unleashed!

Cited by 13 publications

References 30 publications

Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution

Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution

Abstraction for Non-ground Answer Set Programs

Land Evaluation Configuration using Answer Set Programming

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