1,053 research outputs found
Towards a General Argumentation System based on Answer-Set Programming
Within the last years, especially since the work proposed by Dung in 1995, argumentation has emerged as a central issue in Artificial Intelligence. With the so called argumentation frameworks (AFs) it is possible to represent statements (arguments) together with a binary attack relation between them.
The conflicts between the statements are solved on a semantical level by selecting acceptable sets of arguments. An increasing amount of data requires an automated computation of such solutions.
Logic Programming in particular Answer-Set Programming (ASP) turned out to be adequate to solve problems associated to such AFs.
In this work we use ASP to design a sophisticated system for the evaluation of several types of argumentation frameworks
A Unifying Framework for Learning Argumentation Semantics
Argumentation is a very active research field of Artificial Intelligence
concerned with the representation and evaluation of arguments used in dialogues
between humans and/or artificial agents. Acceptability semantics of formal
argumentation systems define the criteria for the acceptance or rejection of
arguments. Several software systems, known as argumentation solvers, have been
developed to compute the accepted/rejected arguments using such criteria. These
include systems that learn to identify the accepted arguments using
non-interpretable methods. In this paper we present a novel framework, which
uses an Inductive Logic Programming approach to learn the acceptability
semantics for several abstract and structured argumentation frameworks in an
interpretable way. Through an empirical evaluation we show that our framework
outperforms existing argumentation solvers, thus opening up new future research
directions in the area of formal argumentation and human-machine dialogues
SMT-based Verification of LTL Specifications with Integer Constraints and its Application to Runtime Checking of Service Substitutability
An important problem that arises during the execution of service-based
applications concerns the ability to determine whether a running service can be
substituted with one with a different interface, for example if the former is
no longer available. Standard Bounded Model Checking techniques can be used to
perform this check, but they must be able to provide answers very quickly, lest
the check hampers the operativeness of the application, instead of aiding it.
The problem becomes even more complex when conversational services are
considered, i.e., services that expose operations that have Input/Output data
dependencies among them. In this paper we introduce a formal verification
technique for an extension of Linear Temporal Logic that allows users to
include in formulae constraints on integer variables. This technique applied to
the substitutability problem for conversational services is shown to be
considerably faster and with smaller memory footprint than existing ones
Performance evaluation of word-aligned compression methods for bitmap indices
Bitmap indices are a widely used scheme for large read-only repositories in data warehouses and scientific databases. This binary representation allows the use of bit-wise operations for fast query processing and is typically compressed using run-length encoding techniques. Most bitmap compression techniques are aligned using a fixed encoding length (32 or 64 bits) to avoid explicit decompression during query time. They have been proposed to extend or enhance word-aligned hybrid (WAH) compression. This paper presents a comparative study of four bitmap compression techniques: WAH, PLWAH, CONCISE, and EWAH. Experiments are targeted to identify the conditions under which each method should be applied and quantify the overhead incurred during query processing. Performance in terms of compression ratio and query time is evaluated over synthetic-generated bitmap indices, and results are validated over bitmap indices generated from real data sets. Different query optimizations are explored, query time estimation formulas are defined, and the conditions under which one method should be preferred over another are formalized
Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs
This paper presents a method for inducing logic programs from examples that
learns a new class of concepts called first-order decision lists, defined as
ordered lists of clauses each ending in a cut. The method, called FOIDL, is
based on FOIL (Quinlan, 1990) but employs intensional background knowledge and
avoids the need for explicit negative examples. It is particularly useful for
problems that involve rules with specific exceptions, such as learning the
past-tense of English verbs, a task widely studied in the context of the
symbolic/connectionist debate. FOIDL is able to learn concise, accurate
programs for this problem from significantly fewer examples than previous
methods (both connectionist and symbolic).Comment: See http://www.jair.org/ for any accompanying file
Enforcement in Abstract Argumentation via Boolean Optimization
Computational aspects of argumentation are a central research topic of modern artificial intelligence. A core formal model for argumentation, where the inner structure of arguments is abstracted away, was provided by Dung in the form of abstract argumentation frameworks (AFs). AFs are syntactically directed graphs with the nodes representing arguments and edges representing attacks between them. Given the AF, sets of jointly acceptable arguments or extensions are defined via different semantics. The computational complexity and algorithmic solutions to so-called static problems, such as the enumeration of extensions, is a well-studied topic.
Since argumentation is a dynamic process, understanding the dynamic aspects of AFs is also important. However, computational aspects of dynamic problems have not been studied thoroughly. This work concentrates on different forms of enforcement, which is a core dynamic problem in the area of abstract argumentation. In this case, given an AF, one wants to modify it by adding and removing attacks in a way that a given set of arguments becomes an extension (extension enforcement) or that given arguments are credulously or skeptically accepted (status enforcement).
In this thesis, the enforcement problem is viewed as a constrained optimization task where the change to the attack structure is minimized. The computational complexity of the extension and status enforcement problems is analyzed, showing that they are in the general case NP-hard optimization problems. Motivated by this, algorithms are presented based on the Boolean optimization paradigm of maximum satisfiability (MaxSAT) for the NP-complete variants, and counterexample-guided abstraction refinement (CEGAR) procedures, where an interplay between MaxSAT and Boolean satisfiability (SAT) solvers is utilized, for problems beyond NP. The algorithms are implemented in the open source software system Pakota, which is empirically evaluated on randomly generated enforcement instances
Solving Set Optimization Problems by Cardinality Optimization with an Application to Argumentation
Optimization—minimization or maximization—in the lattice of subsets is a frequent operation in Artificial Intelligence tasks. Examples are subset-minimal model-based diagnosis, nonmonotonic reasoning by means of circumscription, or preferred extensions in abstract argumentation. Finding the optimum among many admissible solutions is often harder than finding admissible solutions with respect to both computational complexity and methodology. This paper addresses the former issue by means of an effective method for finding subset-optimal solutions. It is based on the relationship between cardinality-optimal and subset-optimal solutions, and the fact that many logic-based declarative programming systems provide constructs for finding cardinality-optimal solutions, for example maximum satisfiability (MaxSAT) or weak constraints in Answer Set Programming (ASP). Clearly each cardinality-optimal solution is also a subset-optimal one, and if the language also allows for the addition of particular restricting constructs (both MaxSAT and ASP do) then all subset-optimal solutions can be found by an iterative computation of cardinality-optimal solutions. As a showcase, the computation of preferred extensions of abstract argumentation frameworks using the proposed method is studied
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