On the issue of contraposition of defeasible rules

The past ten years have shown a great variety of approaches for formal argumentation. An interesting question is to which extent these various formalisms correspond to the different application domains. That is, does the appropriate argumentation formalism depend on the particular domain of application, or does “one size fits all”. In this paper, we study this question from the perspective of one relatively simple design consideration: should or should there not be contrapostion of (or modus tollens) on defeasible rules. We aim to show that the answer depends on whether one is considering epistemical or constitutive reasoning, and that hence different domains require fundamentally different forms of defeasible reasoning

Rationality postulates: applying argumentation theory for non-monotonic reasoning

The current book chapter examines how to apply Dung’s theory of abstract argumentation to define meaningful forms of nonmonotonic inference. The idea is that arguments are constructed using strict and defeasible inference rules, and that it is then examined how these arguments attack (or defeat) each other. The thus defined argumentation framework provides the basis for applying Dung-style semantics, yielding a number of extensions of arguments. As each of the constructed arguments has a conclusion, an extension of arguments has an associated extension of conclusions. It are these extensions of conclusions that we are interested in. In particular, we ask ourselves whether each of these extensions is (1) consistent, (2) closed under the strict inference rules and (3) free from undesired interference. We examine the current generation of techniques to satisfy these properties, and identify some research issues that are yet to be dealt with

On resolving conflicts between arguments

Argument systems are based on the idea that one can construct arguments for propositions; i.e., structured reasons justifying the belief in a proposition. Using defeasible rules, arguments need not be valid in all circumstances, therefore, it might be possible to construct an argument for a proposition as well as its negation. When arguments support conflicting propositions, one of the arguments must be defeated, which raises the question of \emph{which (sub-)arguments can be subject to defeat}? In legal argumentation, meta-rules determine the valid arguments by considering the last defeasible rule of each argument involved in a conflict. Since it is easier to evaluate arguments using their last rules, \emph{can a conflict be resolved by considering only the last defeasible rules of the arguments involved}? We propose a new argument system where, instead of deriving a defeat relation between arguments, \emph{undercutting-arguments} for the defeat of defeasible rules are constructed. This system allows us, (\textit{i}) to resolve conflicts (a generalization of rebutting arguments) using only the last rules of the arguments for inconsistencies, (\textit{ii}) to determine a set of valid (undefeated) arguments in linear time using an algorithm based on a JTMS, (\textit{iii}) to establish a relation with Default Logic, and (\textit{iv}) to prove closure properties such as \emph{cumulativity}. We also propose an extension of the argument system that enables \emph{reasoning by cases}

For the sake of the Argument : explorations into argument-based reasoning

Riet, R.P. van de [Promotor]Prakken, H. [Copromotor

A Purely Defeasible Argumentation Framework

Argumentation theory is concerned with the way that intelligent agents discuss whether some statement holds. It is a claim-based theory that is widely used in many areas, such as law, linguistics and computer science. In the past few years, formal argumentation frameworks have been heavily studied and applications have been proposed in fields such as natural language processing, the semantic web and multi-agent systems. Studying argumentation provides results which help in developing tools and applications in these areas. Argumentation is interesting as a logic-based approach to deal with inconsistent information. Arguments are constructed using a process like logical inference, with inconsistencies giving rise to conflicts between arguments. These conflicts can then be handled by well-founded means, giving a consistent set of well-justified arguments and conclusions. Dung\u27s seminal work tells us how to handle the conflicts between arguments. However, it says nothing about the structure of arguments, or how to construct arguments and attack relationships from a knowledge base. ASPIC+ is one of the most widely used systems for structured arguments. However, there are some limitations on ASPIC+ if it is to satisfy widely accepted standards of rationality. Since most of these limitations are due to the use of strict rules, it is worth considering using a purely defeasible subset of ASPIC+. The main contribution of this dissertation is the purely defeasible argumentation framework ASPIC+D. There are three research questions related to this topic which are investigated here: (1) Do we lose anything in removing the strict elements? (2) Do purely defeasible version of theories generate the same results as the original theories? (3) What do we gain by removing the strict elements? I show that using ASPIC+D, it is possible, in a well-defined sense, to capture the same information as using ASPIC+ with strict rules. In particular, I prove that under some reasonable assumptions, it is possible to take a well-defined theory in ASPIC+, that is one with a consistent set of conclusions, and translate it into ASPIC+D such that, under the grounded semantics, we obtain the same set of justified conclusions. I also show that, under some additional assumptions, the same is true under any complete-based semantics. Furthermore, I formally characterize the situations in which translating an ASPIC+ theory that is ill-defined into ASPIC+D will lead to the same sets of justified conclusions. In doing this I deal both with ASPIC+ theories that are not closed under transposition and theories that are axiom inconsistent. At last, I analyze the two systems in the context of the non-monotonic axioms. I show that ASPIC+ and ASPIC+D satisfy exactly same axioms under what I call the “argument construction” interpretation and the “justified conclusions” interpretation under the grounded semantics. Furthermore, because of the lack of strict elements, ASPIC+ satisfies more of the non-monotonic axioms than ASPIC+ in the ``justified conclusions\u27\u27 interpretation under the preferred semantic. This means that ASPIC+ and ASPIC+D may not have the same justified conclusions under the preferred semantics

Working on the Argument Pipeline: Through Flow Issues between Natural Language Argument, Instantiated Arguments, and Argumentation Frameworks

In many domains of public discourse such as arguments about public policy, there is an abundance of knowledge to store, query, and reason with. To use this knowledge, we must address two key general problems: first, the problem of the knowledge acquisition bottleneck between forms in which the knowledge is usually expressed, e.g., natural language, and forms which can be automatically processed; second, reasoning with the uncertainties and inconsistencies of the knowledge. Given such complexities, it is labour and knowledge intensive to conduct policy consultations, where participants contribute statements to the policy discourse. Yet, from such a consultation, we want to derive policy positions, where each position is a set of consistent statements, but where positions may be mutually inconsistent. To address these problems and support policy-making consultations, we consider recent automated techniques in natural language processing, instantiating arguments, and reasoning with the arguments in argumentation frameworks. We discuss application and “bridge” issues between these techniques, outlining a pipeline of technologies whereby: expressions in a controlled natural language are parsed and translated into a logic (a literals and rules knowledge base), from which we generate instantiated arguments and their relationships using a logic-based formalism (an argument knowledge base), which is then input to an implemented argumentation framework that calculates extensions of arguments (an argument extensions knowledge base), and finally, we extract consistent sets of expressions (policy positions). The paper reports progress towards reasoning with web-based, distributed, collaborative, incomplete, and inconsistent knowledge bases expressed in natural language

Embedding abduction in nonmonotonic theories

An important ampliative inference schema that is commonly used is abduction. Abduction plays a central role in many applications, such as diagnosis, expert systems, and causal reasoning. In a very broad sense we can state that abduction is the inference process that goes from observations to explanations within a more general context or theoretical framework. That is to say, abductive inference looks for sentences (named explanations), which, added to the theory, enable deductions for the observations. Most of the times there are several such explanations for a given observation. For this reason, in a narrower sense, abduction is regarded as an inference to the best explanation. However, a problem that faces abduction is the explanation of anomalous observations, i. e., observations that are contradictory with the current theory. It is perhaps impossible to do such inferences in monotonic theories. For this reason, in this work we will consider the problem of characterizing abduction in nonmonotonic theories. Our inference system is based on a natural deduction presentation of the implicational segment of a relevant logic, much similar to the R! system of Anderson and Belnap. Then we will discuss some issues arising the pragmatic acceptance of abductive inferences in nonmonotonic theories.Eje: Aspectos teóricos de inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Defeasible Reasoning in SROEL: from Rational Entailment to Rational Closure

In this work we study a rational extension $SROEL^R T$ of the low complexity description logic SROEL, which underlies the OWL EL ontology language. The extension involves a typicality operator T, whose semantics is based on Lehmann and Magidor's ranked models and allows for the definition of defeasible inclusions. We consider both rational entailment and minimal entailment. We show that deciding instance checking under minimal entailment is in general $\Pi^P_2$-hard, while, under rational entailment, instance checking can be computed in polynomial time. We develop a Datalog calculus for instance checking under rational entailment and exploit it, with stratified negation, for computing the rational closure of simple KBs in polynomial time.Comment: Accepted for publication on Fundamenta Informatica

Defeasible Logic Programming: An Argumentative Approach

The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query q will succeed when there is an argument A for q that is warranted, ie, the argument A that supports q is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent's knowledge and for providing an argumentation based reasoning mechanism to agents.Comment: 43 pages, to appear in the journal "Theory and Practice of Logic Programming