Gerardo I. Simari

KR Conference 2025 Conference Paper

A Principle-based Framework for Analyzing Dialogue Game-based Semantics

• Yamil O. Soto
• Andrea Cohen
• Cristhian Ariel D. Deagustini
• Maria Vanina Martinez
• Gerardo I. Simari

The dialogue game-based approach to argumentation semantics proposes to determine the acceptance status of arguments through two-party zero-sum dialogue games. Furthermore, by selecting different sets of rules to govern the moves of arguments in the game, it allows for the characterization of distinct argumentation semantics. This approach has proven significant for theoretical and practical reasons. Accordingly, the ability to identify the most suitable semantics for a given domain is a key element in promoting the adoption of dialogue game-based semantics in real-world systems. This paper introduces a set of principles for systematically analyzing dialogue game-based semantics. We aim to contribute to existing frameworks by enabling a deeper understanding of the theoretical foundations of such argumentation semantics. In doing so, our framework may also guide the development of new dialogue game-based semantics.

FLAP Journal 2025 Journal Article

A Unifying Framework for Probabilistic Argumentation

• Gianvincenzo Alfano
• Mario A. Leiva
• Gerardo I. Simari

Formal argumentation has attracted significant attention in the field of Knowledge Representation and Reasoning over the past two decades. Dung’s Argumentation Framework (AF) has been extended in various directions, in- cluding approaches that incorporate quantified uncertainty regarding the exis- tence of arguments and attacks. However, comparatively less effort has been devoted to integrating probabilistic reasoning into structured argumentation or other extensions of AF. In this paper, we introduce the Unified Probabilistic Argumentation Framework (UPAF), a general and expressive theoretical model capable of capturing a wide range of existing argumentation formalisms. UPAF can be equipped with an environmental model that assigns (not necessarily in- dependent) probabilistic events to elements of the underlying argumentation structure, thus enabling reasoning under uncertainty. We demonstrate that UPAF can encode classical and extended forms of Dung’s framework, as well as structured argumentation frameworks such as Assumption-Based Argumenta- tion and Defeasible Logic Programming. Finally, we discuss the computational complexity of key reasoning tasks within UPAF instances.

FLAP Journal 2025 Journal Article

Defeasible Argumentation-based Epistemic Planning with Preferences

• Juan C. L. Teze
• Lluis Godo
• Gerardo I. Simari

Many real-world applications of intelligent systems involve solving planning problems of different nature, oftentimes in dynamic environments and having to deal with potentially contradictory information, leading to what is com- monly known as epistemic planning. In this context, defeasible argumentation is a powerful tool that has been developed for over three decades as a practi- cal mechanism that allows for flexible handling of preferences and explainable reasoning. In this article, we first motivate the need to develop argumentation- based epistemic planning frameworks that can be leveraged in real-world ap- plications, describe the related literature, and then provide an overview of a recently-proposed approach to incorporate defeasible argumentation and pref- erences into automated planning processes. In particular, the framework in- corporates conditional expressions to select and change priorities regarding in- formation upon which plans are constructed. We describe its main properties, analyze its strengths and limitations using an illustrative use case, and discuss several future research directions that can be taken to further develop it.

AIJ Journal 2022 Journal Article

Inconsistency-tolerant query answering for existential rules

• Thomas Lukasiewicz
• Enrico Malizia
• Maria Vanina Martinez
• Cristian Molinaro
• Andreas Pieris
• Gerardo I. Simari

Querying inconsistent knowledge bases is an intriguing problem that gave rise to a flourishing research activity in the knowledge representation and reasoning community during the last years. It has been extensively studied in the context of description logics (DLs), and its computational complexity is rather well-understood. Although DLs are popular formalisms for modeling ontologies, it is generally agreed that rule-based ontologies are well-suited for data-intensive applications, since they allow us to conveniently deal with higher-arity relations, which naturally occur in standard relational databases. The goal of this work is to perform an in-depth complexity analysis of querying inconsistent knowledge bases in the case of the main decidable classes of existential rules, based on the notions of guardedness, linearity, acyclicity, and stickiness, enriched with negative (a. k. a. denial) constraints. Our investigation concentrates on three central inconsistency-tolerant semantics: the ABox repair (AR) semantics, considered as the standard one, and its main sound approximations, the intersection of repairs (IAR) semantics and the intersection of closed repairs (ICR) semantics.

IS Journal 2021 Journal Article

Guest Editorial Argumentation-Based Reasoning

• Francesco Parisi
• Gerardo I. Simari

The papers in this special section focus on augmentation-based reasoning. Real-world knowledge-based systems must deal with information coming from different sources, leading to uncertainty due to incompleteness, inconsistency, and/or inherent uncertainty (such as the uncertainty present in very complex systems such as the stock market or the weather). Instead of considering such uncertain information to be useless, knowledge engineers face the challenge of putting it to good use when solving a wide range of problems. Argumentation is a useful approach in this setting: Reasons for and against a claim are analyzed to decide on an outcome, much in the same way as organized human discussions are carried out. 1–5 An important byproduct of such analyses is an accompanying explanation that can be leveraged to decide if there is information that should be used differently, discarded, or there is further information to be contemplated.

AIJ Journal 2021 Journal Article

Incremental computation for structured argumentation over dynamic DeLP knowledge bases

• Gianvincenzo Alfano
• Sergio Greco
• Francesco Parisi
• Gerardo I. Simari
• Guillermo R. Simari

Structured argumentation systems, and their implementation, represent an important research subject in the area of Knowledge Representation and Reasoning. Structured argumentation advances over abstract argumentation frameworks by providing the internal construction of the arguments that are usually defined by a set of (strict and defeasible) rules. By considering the structure of arguments, it becomes possible to analyze reasons for and against a conclusion, and the warrant status of such a claim in the context of a knowledge base represents the main output of a dialectical process. Computing such statuses is a costly process, and any update to the knowledge base could potentially have a huge impact if done naively. In this work, we investigate the case of updates consisting of both additions and removals of pieces of knowledge in the Defeasible Logic Programming (DeLP) framework, first analyzing the complexity of the problem and then identifying conditions under which we can avoid unnecessary computations—central to this is the development of structures (e. g. graphs) to keep track of which results can potentially be affected by a given update. We introduce a technique for the incremental computation of the warrant statuses of conclusions in DeLP knowledge bases that evolve due to the application of (sets of) updates. We present the results of a thorough experimental evaluation showing that our incremental approach yields significantly faster running times in practice, as well as overall fewer recomputations, even in the case of sets of updates performed simultaneously.

JAIR Journal 2021 Journal Article

Labeled Bipolar Argumentation Frameworks

• Melisa G. Escañuela Gonzalez
• Maximiliano C. D. Budán
• Gerardo I. Simari
• Guillermo R. Simari

An essential part of argumentation-based reasoning is to identify arguments in favor and against a statement or query, select the acceptable ones, and then determine whether or not the original statement should be accepted. We present here an abstract framework that considers two independent forms of argument interaction—support and conflict—and is able to represent distinctive information associated with these arguments. This information can enable additional actions such as: (i) a more in-depth analysis of the relations between the arguments; (ii) a representation of the user’s posture to help in focusing the argumentative process, optimizing the values of attributes associated with certain arguments; and (iii) an enhancement of the semantics taking advantage of the availability of richer information about argument acceptability. Thus, the classical semantic definitions are enhanced by analyzing a set of postulates they satisfy. Finally, a polynomial-time algorithm to perform the labeling process is introduced, in which the argument interactions are considered.

FLAP Journal 2021 Journal Article

On the Incremental Computation of Semantics in Dynamic Argumentation.

• Gianvincenzo Alfano
• Sergio Greco
• Francesco Parisi
• Gerardo I. Simari
• Guillermo Ricardo Simari

Argumentation frameworks often model dynamic situations where arguments and their relationships (e.g., attacks) frequently change over time. As a consequence, the sets of conclusions (e.g., extensions of abstract argumentation frameworks, or warranted literals for structured argumentation frameworks) often need to be computed again after performing an update. However, as most of the argumentation semantics proposed so far suffer from high computational complexity, computing the set of conclusions from scratch is costly in general. In this work, we address the problems of efficiently recomputing extensions of dynamic abstract argumentation frameworks and warranted literals in dynamic defeasible knowledge bases. In particular, we first present an incremental algorithmic solution whose main idea is that of using an initial extension and the update to identify a (potentially small) portion of an abstract argumentation framework, which is sufficient to compute an extension of the updated framework.

IS Journal 2020 Journal Article

Guidelines for the Analysis and Design of Argumentation-Based Recommendation Systems

• Mario Leiva
• Maximiliano C. D. Budan
• Gerardo I. Simari

Recommender systems study the characteristics of its users and applying different kinds of processing to the available data, find a subset of items that may be of interest to a given user in a specific situation. Argumentation-based tools offer the possibility of analyzing complex and dynamic domains by generating and analyzing arguments for and against recommending a specific item based on the users’ preferences. This approach allows us to analyze the qualitative and quantitative characteristics of the recommended items, and to provide explanations to increase transparency. In this article, we develop a set of software engineering guidelines for the analysis and design of recommender systems leveraging this approach.

IJCAI Conference 2019 Conference Paper

From Data to Knowledge Engineering for Cybersecurity

• Gerardo I. Simari

Data present in a wide array of platforms that are part of today's information systems lies at the foundation of many decision making processes, as we have now come to depend on social media, videos, news, forums, chats, ads, maps, and many other data sources for our daily lives. In this article, we first discuss how such data sources are involved in threats to systems' integrity, and then how they can be leveraged along with knowledge-based tools to tackle a set of challenges in the cybersecurity domain. Finally, we present a brief discussion of our roadmap for research and development in the near future to address the set of ever-evolving cyber threats that our systems face every day.

IS Journal 2016 Journal Article

AI's 10 to Watch

• Haris Aziz
• Elias Bareinboim
• Yejin Choi
• Daniel Hsu
• Shivaram Kalyanakrishnan
• Reshef Meir
• Suchi Saria
• Gerardo I. Simari

IEEE Intelligent Systems once again selected 10 young AI scientists as " AI's 10 to Watch. " This acknowledgment and celebration not only recognizes these young scientists and makes a positive impact in their academic career but also promotes the community and cutting-edge AI research among next-generation AI researchers, the industry, and the general public alike. The contributions are "Collective Decision Making in Multi-Agent Systems, " by Haris Aziz, "From Causal Inference and Data Fusion to an Automated Scientist, " by Elias Bareinboim, "Language, Vision, and Social AI, " by Yejin Choi, "Algorithms for Machine Learning, " by Daniel Hsu, "Learning Agents, " by Shivaram Kalyanakrishnan, "Strategy and Bounded Rationality, " by Reshef Meir, "; A Reasoning Engine for Tailoring Healthcare to the Individual, " by Suchi Saria, "Pushing the Limits of Knowledge Representation and Reasoning, " by Gerardo I. Simari, "Better Group Decision Making, " by Lirong Xia, and "Distributed Constraint Optimization, " by William Yeoh.

AAAI Conference 2016 Conference Paper

Basic Probabilistic Ontological Data Exchange with Existential Rules

• Thomas Lukasiewicz
• Maria Vanina Martinez
• Livia Predoiu
• Gerardo I. Simari

We study the complexity of exchanging probabilistic data between ontology-based probabilistic databases. We consider the Datalog+/– family of languages as ontology and ontology mapping languages, and we assume different compact encodings of the probabilities of the probabilistic source databases via Boolean events. We provide an extensive complexity analysis of the problem of deciding the existence of a probabilistic (universal) solution for a given probabilistic source database relative to a (probabilistic) data exchange problem for the different languages considered.

IJCAI Conference 2015 Conference Paper

Combining Existential Rules with the Power of CP-Theories

• Tommaso Di Noia
• Thomas Lukasiewicz
• Maria Vanina Martinez
• Gerardo I. Simari
• Oana Tifrea-Marciuska

The tastes of a user can be represented in a natural way by using qualitative preferences. In this paper, we explore how ontological knowledge expressed via existential rules can be combined with CP-theories to (i) represent qualitative preferences along with domain knowledge, and (ii) perform preference-based answering of conjunctive queries (CQs). We call these combinations ontological CP-theories (OCP-theories). We define skyline and k-rank answers to CQs based on the user’s preferences encoded in an OCP-theory, and provide an algorithm for computing them. We also provide precise complexity (including data tractability) results for deciding consistency, dominance, and CQ skyline membership for OCP-theories.

ECAI Conference 2014 Conference Paper

Probabilistic Preference Logic Networks

• Thomas Lukasiewicz
• Maria Vanina Martinez
• Gerardo I. Simari

Reasoning about an entity's preferences (be it a user of an application, an individual targeted for marketing, or a group of people whose choices are of interest) has a long history in different areas of study. In this paper, we adopt the point of view that grows out of the intersection of databases and knowledge representation, where preferences are usually represented as strict partial orders over the set of tuples in a database or the consequences of a knowledge base. We introduce probabilistic preference logic networks (PPLNs), which flexibly combine such preferences with probabilistic uncertainty. Their applications are clear in domains such as the Social Semantic Web, where users often express preferences in an incomplete manner and through different means, many times in contradiction with each other. We show that the basic problems associated with reasoning with PPLNs (computing the probability of a world or a given query) are #P-hard, and then explore ways to make these computations tractable by: (i) leveraging results from order theory to obtain a polynomial-time randomized approximation scheme (FPRAS) under fixed-parameter assumptions; and (ii) studying a fragment of the language of PPLNs for which exact computations can be performed in fixed-parameter polynomial time.

AAMAS Conference 2013 Conference Paper

MANCaLog: A Logic for Multi-Attribute Network Cascades

• Paulo Shakarian
• Gerardo I. Simari
• Robert Schroeder

IJCAI Conference 2013 Conference Paper

Preference-Based Query Answering in Datalog+/– Ontologies

• Thomas Lukasiewicz
• Maria Vanina Martinez
• Gerardo I. Simari

The study of preferences has a long tradition in many disciplines, but it has only relatively recently entered the realm of data management through their application in answering queries to relational databases. The current revolution in data availability through the Web and, perhaps most importantly in the last few years, social media sites and applications, puts ontology languages at the forefront of data and information management technologies. In this paper, we propose the first (to our knowledge) integration of ontology languages with preferences as in relational databases by developing PrefDatalog+/–, an extension of the Datalog+/– family of languages with preference management formalisms closely related to those previously studied for relational databases. We focus on two kinds of answers to queries that are relevant to this setting, skyline and k-rank (a generalization of top-k queries), and develop algorithms for computing these answers to both DAQs (disjunctions of atomic queries) and CQs (conjunctive queries). We show that DAQ answering in PrefDatalog+/– can be done in polynomial time in the data complexity, as in relational databases, as long as query answering can also be done in polynomial time (in the data complexity) in the underlying classical ontology.

UAI Conference 2012 Conference Paper

Heuristic Ranking in Tightly Coupled Probabilistic Description Logics

• Thomas Lukasiewicz
• Maria Vanina Martinez
• Giorgio Orsi 0001
• Gerardo I. Simari

The Semantic Web effort has steadily been gaining traction in the recent years. In particular, Web search companies are recently realizing that their products need to evolve towards having richer semantic search capabilities. Description logics (DLs) have been adopted as the formal underpinnings for Semantic Web languages used in describing ontologies. Reasoning under uncertainty has recently taken a leading role in this arena, given the nature of data found on the Web. In this paper, we present a probabilistic extension of the DL EL++ (which underlies the OWL2 EL profile) using Markov logic networks (MLNs) as probabilistic semantics. This extension is tightly coupled, meaning that probabilistic annotations in formulas can refer to objects in the ontology. We show that, even though the tightly coupled nature of our language means that many basic operations are data-intractable, we can leverage a sublanguage of MLNs that allows to rank the atomic consequences of an ontology relative to their probability values (called ranking queries) even when these values are not fully computed. We present an anytime algorithm to answer ranking queries, and provide an upper bound on the error that it incurs, as well as a criterion to decide when results are guaranteed to be correct.

ECAI Conference 2012 Conference Paper

Inconsistency Handling in Datalog+/- Ontologies

• Thomas Lukasiewicz
• Maria Vanina Martinez
• Gerardo I. Simari

The advent of the Semantic Web has made the problem of inconsistency management especially relevant. Datalog+/− is a family of ontology languages that is in particular useful for representing and reasoning over lightweight ontologies in the Semantic Web. In this paper, we study different semantics for query answering in inconsistent Datalog+/− ontologies. We develop a general framework for inconsistency management in Datalog+/− ontologies based on incision functions from belief revision, in which we can characterize several query answering semantics as special cases: (i) consistent answers, originally developed for relational databases and recently adopted for some classes of description logics (DLs), (ii) intersection semantics, a sound approximation of consistent answers, and (iii) lazy consistent answers, a novel alternative semantics that offers a good compromise between quality of answers and computation time. We also provide complexity results for query answering under the different semantics, including data tractability results.

KR Conference 2008 Conference Paper

Inconsistency Management Policies

• Maria Vanina Martinez
• Francesco Parisi
• Andrea Pugliese
• Gerardo I. Simari
• V. S. Subrahmanian

Though there is much work on how inconsistency in databases should be managed, there is good reason to believe that end users will want to bring their domain expertise and needs to bear in how to deal with inconsistencies. In this paper, we propose the concept of Inconsistency Management Policies (IMPs). We show that IMPs are rich enough to specify many types of inconsistency management methods proposed previously, but provide end users with tools that allow them to use the policies that they want. Our policies are also capable of allowing inconsistency to persist in the database or of eliminating more than a minimal subset of tuples involved in the inconsistency. We present a formal axiomatic definition of IMPs and present appropriate complexity results, together with results linking different IMPs together. We extend the relational algebra (RA) to incorporate IMPs and present theoretical results showing how IMPs and classical RA operators interact.

KR Conference 2008 Conference Paper

Promises Kept, Promises Broken: An Axiomatic and Quantitative Treatment of Fulfillment

• Gerardo I. Simari
• Matthias Broecheler
• V. S. Subrahmanian
• Sarit Kraus

In this paper, we propose a theoretical framework within which to evaluate the reliability of promises that an agent makes, based on past performance of the agent. Our framework does not just propose one such measure, but defines axioms that govern the choice of measure. The framework is able to account for partial fulfillment of promises, late fulfillment of promises, fulfillment of variants of promises, and the like. Within this framework, we propose some specific measures to evaluate promises made by agents and develop algorithms to compute these efficiently. We tested our methods on a real world data set of airline flight information and show that our methods are both accurate and quickly computable, even on large data sets.

IS Journal 2007 Journal Article

CARA: A Cultural-Reasoning Architecture

• V.S. Subrahmanian
• Massimiliano Albanese
• Maria Vanina Martinez
• Dana Nau
• Diego Reforgiato
• Gerardo I. Simari
• Amy Sliva
• Jonathan Wilkenfeld

There's a constant need to reason about diverse cultures all over the world. Past cultural-reasoning research has focused primarily on techniques to organize, catalog, and reason about cultural and historical artifacts of the kind typically stored in a museum. This is extremely valuable. However, the term "cultural reasoning" as we use it in the previous examples (and in this article) focuses on understanding how different cultural groups today make decisions and what factors those decisions are based on. An architecture that supports cultural reasoning should, for example, be able to pinpoint characteristics that differentiate organizations engaging political action within legitimate frameworks from those engaging in violence and terror. Key in all this is that cultural reasoning must go hand in hand with environmental reasoning. We believe that any architecture to support cultural reasoning about a given group, political entity, business, or religious organization should contain these components: 1) a semantic Web extraction engine to elicit data about the organization, 2) an opinion-mining engine that captures the organization's opinions, 3) an algorithm to correlate environmental variables with actions that the organization takes, and 4) a simulation or game environment within which analysts and users can see what the organization has done and what it might do in hypothetical situations