Zohreh Shams

Reasoning with Concept Diagrams About Antipatterns in Ontologies

Ontologies are notoriously hard to define, express and reason about. Many tools have been developed to ease the ontology debugging and reasoning, however they often lack accessibility and formalisation. A visual representation language, concept diagrams, was developed for expressing ontologies, which has been empirically proven to be cognitively more accessible to ontology users. In this paper we answer the question of “How can concept diagrams be used to reason about inconsistencies and incoherence of ontologies?”. We do so by formalising a set of inference rules for concept diagrams that enables stepwise verification of the inconsistency and incoherence of a set of ontology axioms. The design of inference rules is driven by empirical evidence that concise (merged) diagrams are easier to comprehend for users than a set of lower level diagrams that are a one-to-one translation from OWL ontology axioms. We prove that our inference rules are sound, and exemplify how they can be used to reason about inconsistencies and incoherence.

Accessible Reasoning with Diagrams: from Cognition to Automation

High-tech systems are ubiquitous and often safety and security critical: reasoning about their correctness is paramount. Thus, precise modelling and formal reasoning are necessary in order to convey knowledge unambiguously and accurately. Whilst mathematical modelling adds great rigour, it is opaque to many stakeholders which leads to errors in data handling, delays in product release, for example. This is a major motivation for the development of diagrammatic approaches to formalisation and reasoning about models of knowledge. In this paper, we present an interactive theorem prover, called iCon, for a highly expressive diagrammatic logic that is capable of modelling OWL 2 ontologies and, thus, has practical relevance. Significantly, this work is the first to design diagrammatic inference rules using insights into what humans find accessible. Specifically, we conducted an experiment about relative cognitive benefits of primitive (small step) and derived (big step) inferences, and use the results to guide the implementation of inference rules in iCon.

Practical reasoning with norms for autonomous software agents

Abstract Autonomous software agents operating in dynamic environments need to constantly reason about actions in pursuit of their goals, while taking into consideration norms which might be imposed on those actions. Normative practical reasoning supports agents making decisions about what is best for them to (not) do in a given situation. What makes practical reasoning challenging is the interplay between goals that agents are pursuing and the norms that the agents are trying to uphold. We offer a formalisation to allow agents to plan for multiple goals and norms in the presence of durative actions that can be executed concurrently . We compare plans based on decision-theoretic notions (i.e. utility) such that the utility gain of goals and utility loss of norm violations are the basis for this comparison. The set of optimal plans consists of plans that maximise the overall utility, each of which can be chosen by the agent to execute. We provide an implementation of our proposal in Answer Set Programming, thus allowing us to state the original problem in terms of a logic program that can be queried for solutions with specific properties.

Implementation of normative practical reasoning with durative actions

Autonomous agents operating in a dynamic environment need constantly to reason about actions in pursuit of their goals, while taking into consideration possible norms imposed on those actions. Normative practical reasoning supports agents decision making about what is best for an agent to do in a given situation. What makes practical reasoning challenging is the conflict between goals that the agent is pursuing and the norms that the agent is trying to uphold. We offer a formal model that allows the agents to plan for conflicting goals and norms in presence of durative actions that can be executed concurrently. We compare plans based on decision-theoretic notions (i.e. utility) such that the utility gain of goals and utility loss of norm violations are the basis of this comparison. The set of optimal plans consists of plans that maximise the overall utility, each of which can be chosen by the agent to execute. The formal model is implemented computationally using answer set programming, which in turns permits the statement of the problem in terms of a logic program that can be queried for solutions with specific properties. We demonstrate how a normative practical reasoning problem can be mapped into an answer set program such that the optimal plans of the former can be obtained as the answer sets of the latter.

Human inference beyond syllogisms: an approach using external graphical representations

Research in psychology about reasoning has often been restricted to relatively inexpressive statements involving quantifiers (e.g. syllogisms). This is limited to situations that typically do not arise in practical settings, like ontology engineering. In order to provide an analysis of inference, we focus on reasoning tasks presented in external graphic representations where statements correspond to those involving multiple quantifiers and unary and binary relations. Our experiment measured participants’ performance when reasoning with two notations. The first notation used topological constraints to convey information via node-link diagrams (i.e. graphs). The second used topological and spatial constraints to convey information (Euler diagrams with additional graph-like syntax). We found that topo-spatial representations were more effective for inferences than topological representations alone. Reasoning with statements involving multiple quantifiers was harder than reasoning with single quantifiers in topological representations, but not in topo-spatial representations. These findings are compared to those in sentential reasoning tasks.

A Two-Phase Dialogue Game for Skeptical Preferred Semantics

In this paper we propose a labelling based dialogue game for determining whether a single argument within a Dung argumentation framework is skeptically preferred. Our game consists of two phases, and determines the membership of a single argument within the extension, assuming optimal play by dialogue participants. In the first phase, one player attempts to advance arguments to construct an extension not containing the argument under consideration, while the second phase verifies that the extension is indeed a preferred one. Correctness within this basic game requires perfect play by both players, and we therefore also introduce an overarching game to overcome this limitation.

Argumentation-based Normative Practical Reasoning

Reasoning about what is best for an agent to do in a particular situation is a challenging task. What makes it even more challenging in a dynamic environment is the existence of norms that aim to regulate a self-interested agent’s behaviour. Practical reasoning is reasoning about what to do in a given situation, particularly in the presence of conflicts between the agent’s practical attitude such as goals, plans and norms. In this paper we: (i) introduce a formal model for normative practical reasoning that allows an agent to plan for multiple and potentially conflicting goals and norms at the same time (ii) identify the best plan(s) for the agent to execute by means of argumentation schemes and critical questions (iii) justify the best plan(s) via an argumentation-based persuasion dialogue for grounded semantics.

ArgPROLEG: A Normative Framework for the JUF Theory

In this paper we propose ArgPROLEG, a normative framework for legal reasoning based on PROLEG, an implementation of the Japanese “theory of presupposed ultimate facts”(JUF). This theory was mainly developed with the purpose of modelling the process of decision making by judges in the court. Not having complete and accurate information about each case, makes uncertainty an unavoidable part of decision making for judges. In the JUF theory each party that puts forward a claim, due to associated burden of proof to each claim, it needs to prove it as well. Not being able to provide such a proof for a claim, enables the judges to discard that claim although they might not be certain about the truth. The framework that we offer benefits from the use of argumentation theory as well as normative framework in multi-agent systems, to bring the reasoning closer to the user. The nature of argumentation in dealing with incomplete information on the one hand and being presentable in the form of dialogues on the other hand, has furthered the emergence and popularity of argumentation in modelling legal disputes. In addition, the use of multiple agents allows more flexibility for the behaviour of the parties involved.