Luís Pereira

These are the proceedings of the First International Conference on Compu- tional Logic (CL 2000) which was held at Imperial College in London from 24th to 28th July, 2000. The theme of the conference covered all aspects of the theory, implementation, and application of computational logic, where computational logic is to be understood broadly as the use of logic in computer science. The conference was collocated with the following events: { 6th International Conference on Rules and Objects in Databases (DOOD 2000) { 10th International Workshop on Logic-based Program Synthesis and Tra- formation (LOPSTR 2000) { 10th International Conference on Inductive Logic Programming (ILP 2000). CL 2000 consisted of seven streams: { Program Development (LOPSTR 2000) { Logic Programming: Theory and Extensions { Constraints { Automated Deduction: Putting Theory into Practice { Knowledge Representation and Non-monotonic Reasoning { Database Systems (DOOD 2000) { Logic Programming: Implementations and Applications. The LOPSTR 2000 workshop constituted the program development stream and the DOOD 2000 conference constituted the database systems stream. Each stream had its own chair and program committee, which autonomously selected the papers in the area of the stream. Overall, 176 papers were submitted, of which 86 were selected to be presented at the conference and appear in these proceedings. The acceptance rate was uniform across the streams. In addition, LOPSTR 2000 accepted about 15 extended abstracts to be presented at the conference in the program development stream.

This chapter investigates the appropriateness of Logic Programming-based reasoning to machine ethics, an interdisciplinary field of inquiry that emerges from the need of imbuing autonomous agents with the capacity for moral decision making. The first part of the chapter aims at identifying morality viewpoints, as studied in moral philosophy and psychology, which are amenable to computational modeling, and then mapping them to appropriate Logic Programming-based reasoning features. The identified viewpoints are covered by two morality themes: moral permissibility and the dual-process model. In the second part, various Logic Programming-based reasoning features are applied to model these identified morality viewpoints, via classic moral examples taken off-the-shelf from the literature. For this purpose, our qualm system mainly employs a combination of the Logic Programming features of abduction, updating, and counterfactuals. These features are all supported jointly by Logic Programming tabling mechanisms. The applications are also supported by other existing Logic Programming-based systems, featuring preference handling and probabilistic reasoning, which complement qualm in addressing the morality viewpoints in question.Throughout the chapter, many references to our published work are given, providing further examples and details about each topic. Thus, this chapter can be envisaged as an entry point survey on the employment of Logic Programming for knowledge modelling and technically implementing machine ethics.

Machine ethics is a sprouting interdisciplinary field of enquiry arising from the need of imbuing autonomous agents with some capacity for moral decision-making. Its overall results are not only important for equipping agents with a capacity for moral judgment, but also for helping better understand morality, through the creation and testing of computational models of ethics theories. Computer models have become well defined, eminently observable in their dynamics, and can be transformed incrementally in expeditious ways. We address, in work reported and surveyed here, the emergence and evolution of cooperation in the collective realm. We discuss how our own research with Evolutionary Game Theory (EGT) modelling and experimentation leads to important insights for machine ethics, such as the design of moral machines, multi-agent systems, and contractual algorithms, plus their potential application in human settings too.

This paper supplies a computational model, via Logic Programming (LP), of counterfactual reasoning of autonomous agents with application to morality. Counterfactuals are conjectures about what would have happened had an alternative event occurred. The first contribution of the paper is showing how counterfactual reasoning is modeled using LP, benefiting from LP abduction and updating. The approach is inspired by Pearl’s structural causal model of counterfactuals, where causal direction and conditional reasoning are captured by inferential arrows of rules in LP. Herein, LP abduction hypothesizes background conditions from given evidence or observations, whereas LP updating frame these background conditions as a counterfactual’s context, and then imposes causal interventions on the program through defeasible LP rules. The second contribution it to apply counterfactuals to agent morality using this LP-based approach. We demonstrate its potential for specifying and querying moral issues, by examining viewpoints on moral permissibility via classic moral principles and examples taken from the literature. Application results were validated on a prototype implementing the approach on top of an integrated LP abduction and updating system supporting tabling.

Counterfactuals are conjectures about what would have happened, had an alternative event occurred. It provides lessons for the future by virtue of contemplating alternatives; it permits thought debugging; it supports a justification why different alternatives would have been worse or not better. Typical expressions are: “If only I were taller …”, “I could have been a winner …”, “I would have passed, were it not for …”, “Even if... the same would follow”. Counterfactuals have been well studied in Linguistics, Philosophy, Physics, Ethics, Psychology, Anthropology, and Computation, but not much within Critical Thinking. The purpose of this study is to illustrate counterfactual thinking, through logic program abduction and updating, and inspired by Pearl’s structural theory of counterfactuals, with an original application to morality, a common concern for critical thinking. In summary, we show counterfactual reasoning to be quite useful for critical thinking, namely about moral issues.

In this chapter, we discuss how considered Logic Programming-based reasoning features are synthesized in three different systems: Acorda, Probabilistic EPA, and Qualm. Whereas the development of Qualm is a contribution of this book, Acorda and Probabilistic EPA are two existing systems that have been developed earlier, but not with any specific wide principled implementation of morality in mind. Though these systems share its main feature, viz., abduction, each system concern itself with a particular combination of features. Moreover, their shared feature, abduction, implements different techniques, indicating the progress made in the development of these three systems. The three systems are employed to model, here, for the first time, different issues of considered moral facets, depending on the need of their respective combination of features.

This chapter aims at realizing our conception about representing diverse moral facets in Logic Programming, by modeling several issues pertaining to those moral facets, using the three systems discussed in Chap. 7. The applicability of these systems corresponds with their relevance to the moral issues being modeled. First, Acorda is employed to model moral permissibility, emphasizing the use of integrity constraints in abduction and preferences over abductive scenarios, where several cases of the classic trolley problem are modeled. Second, moral reasoning concerning uncertain actions is modeled, by means of Probabilistic EPA. Third and finally, we demonstrate the use of Qualm for modeling the issue of moral updating and counterfactual moral reasoning.

This chapter addresses the relevance of the ground breaking work of Alan Turing to justify our functionalism stance regarding the modeling of morality. Turing first defined the algorithmic limits of computability, via an effective well-specified mechanism, and showed the generality of his definition by proving its equivalence to other general, but less algorithmic and non-mechanical, more abstract formulations of computability. Interestingly, Turing raised the issue of whether human beings are a measure for his “machines”, and, in mechanizing human cognition, Turing implicitly introduced the modern perspective since known as “functionalism”. According to this paradigm, what counts is the realization of function, independently of the hardware embodying it. Functionalism is employed to scaffold a philosophical perspective on programming machine ethics. Building intelligent machines seeks a partial understanding of the emergence of higher-level properties, like morality. Functionalism holds that the material substrate is not of the essence, and that it suffices to realize equivalent functionality albeit by way of a different material vehicle. The most fruitful inquires into the nature of “mind” or “general intelligence” shall include the use of Artificial Intelligence to simulate complex mental operations.

Bridging capabilities between the two realms, to wit, the individual and collective, helps understand the emergent ethical behavior of agents in groups, and implements them not just in simulations, but in the world of future robots and their swarms. On the basis of preceding chapters, this chapter considers the bridging of these two realms in machine ethics. Subsequently, we ponder over the teachings of human moral evolution in this regard. A final coda foretells a road to be tread, and portends about ethical machines and us.

In this essay we advance the view that analytical epistemology and artificial intelligence are complementary disciplines. Both fields study epistemic relations, but whereas artificial intelligence approaches this subject from the perspective of understanding formal and computational properties of frameworks purporting to model some epistemic relation or other, traditional epistemology approaches the subject from the perspective of understanding the properties of epistemic relations in terms of their conceptual properties. We argue that these two practices should not be conducted in isolation. We illustrate this point by discussing how to represent a class of inference forms found in standard inferential statistics. This class of inference forms is interesting because its members share two properties that are common to epistemic relations, namely defeasibility and paraconsistency. Our modeling of standard inferential statistical arguments exploits results from both logical artificial intelligence and analytical epistemology. We remark how our approach to this modeling problem may be generalized to an interdisciplinary approach to the study of epistemic relation.

In the individual realm part of this book, we are addressing the interplay amongst appropriate LP features to represent moral facets and to reason about them. One such interplay is between LP abduction and updating, both supported with tabling mechanisms. In this chapter, we propose novel approaches for employing tabling in abduction and updating—separately—viz., tabling abductive solutions in contextual abduction and the incremental tabling of fluents for LP updating. These two individual approaches form the components for their subsequent joint tabling technique when combining LP abduction and updating, to be discussed in detail in Chap. 7. The new techniques introduced here, as well as their later joint combination, are general for normal logic programs, and not specific to morality applications. That is, they are of interest in themselves and may be applicable to other domains.

In this chapter, a survey of research in machine ethics is presented, providing the context and the motivation for our investigations. The survey concerns the individual realm of machine ethics, whereas the background to other realm, the collective one, is broached in Chap. 9, namely Sects. 9.1 and 9.2.1. The first realm views computation as a vehicle for representing moral cognition of an agent and its reasoning thereof, which motivates our investigation for employing Logic Programming (LP) knowledge representation and reasoning features with respect to the individual realm of machine ethics. On the other hand, the second realm emphasizes the emergence, in a population, of evolutionarily stable moral norms, of fair and just cooperation, that ably discards free riders and deceivers, to the advantage of the whole evolved population. It provides a motivation of our research for introducing cognitive abilities, such as intention recognition, commitment, revenge, apology, and forgiveness, to reinforce the emergence of cooperation in the collective realm of machine ethics.

This chapter provides necessary background of Logic Programming (LP) used throughout this book, including semantics of Logic Programs (particularly the Stable Model and the Well-Founded Semantics). The subsequent part of this chapter briefly overview considered LP reasoning features: abduction, preferences, probabilistic LP, updating, LP counterfactuals, and tabling. Moreover, the appropriateness of these features for representing and reasoning about diverse issues of moral facets tackled in this book is discussed.

This chapter reports on our literature study in moral philosophy and psychology for choosing conceptual viewpoints close to LP-based reasoning. These viewpoints fall into three moral facets tackled in this book. First, we study moral permissibility, taking into account the Doctrines of Double Effect, Triple Effect, and Scanlonian contractualism. Second, we look into the dual-process model that stresses the interaction between deliberative and reactive processes in delivering moral decisions. Finally, we discuss the role of counterfactual thinking in moral reasoning.

Counterfactuals capture the process of reasoning about a past event that did not occur, namely what would have happened had this event occurred; or, vice-versa, to reason about an event that did occur but what if it had not. In this chapter, we innovatively make use of LP abduction and updating in an implemented procedure for evaluating counterfactuals, taking the established structural approach of Pearl as reference. Our approach concentrates on pure non-probabilistic counterfactual reasoning in LP, resorting to abduction and updating, in order to determine the logical validity of counterfactuals under the Well-Founded Semantics. Nevertheless, the approach is adaptable to other semantics, too. Even though the LP technique introduced in this chapter is relevant for modeling counterfactual moral reasoning, its use is general, not specific to morality.

This chapter addresses the collective realm computationally, using Evolutionary Game Theory in populations of individuals, to report on norms and morality emergence. These populations, to start with, are not equipped with much cognitive capability, and simply act from a predetermined set of actions. Our research has shown that the introduction of cognitive capabilities, such as intention recognition, commitment, apology, forgiveness, and revenge, separately and jointly, reinforce the emergence of cooperation in populations, comparatively to their absence. We then prospect future work concerned with adding guilt.

This book discusses the two realms of machine ethics, a field that is now becoming a pressing concern and receiving wide attention due to its growing importance. It makes a number of original inroads that exhibit a proof of possibility to systematically represent and reason about a variety of issues from the chosen moral facets by means of moral examples taken off-the-shelf from the morality literature. Given the broad dimension of the topic, the contributions in the book touch solely on a dearth of morality issues. Nevertheless, it prepares and opens the way for additional research towards employing various features available in LP-based reasoning and EGT to machine ethics. Moreover, we draw attention to some salient philosophical considerations underpinning and surging from our work.

Counterfactual Thinking is a human cognitive ability studied in a wide variety of domains. It captures the process of reasoning about a past event that did not occur, namely what would have happened had this event occurred, or, otherwise, to reason about an event that did occur but what would ensue had it not. Given the wide cognitive empowerment of counterfactual reasoning in the human individual, the question arises of how the presence of individuals with this capability may improve cooperation in populations of self-regarding individuals. Here we propose a mathematical model, grounded on Evolutionary Game Theory, to examine the population dynamics emerging from the interplay between counterfactual thinking and social learning (i.e., individuals that learn from the actions and success of others) whenever the individuals in the population face a collective dilemma. Our results suggest that counterfactual reasoning fosters coordination in collective action problems occurring in large populations, and has a limited impact on cooperation dilemmas in which coordination is not required. Moreover, we show that a small prevalence of individuals resorting to counterfactual thinking is enough to nudge an entire population towards highly cooperative standards.

This book presents the refereed proceedings of the Sixth European Workshop on Logics in Artificial Intelligence, JELIA '96, held in Evora, Portugal in September/October 1996. The 25 revised full papers included together with three invited papers were selected from 57 submissions. Many relevant aspects of AI logics are addressed. The papers are organized in sections on automated reasoning, modal logics, applications, nonmonotonic reasoning, default logics, logic programming, temporal and spatial logics, and belief revision and paraconsistency.

This book constitutes the refereed proceedings of the European Workshop on Logics in Artificial Intelligence, JELIA 2000, held in Malaga, Spain in September/October 2000. The 24 revised full papers presented together with three invited papers were carefully reviewed and selected out of 60 submissions. The papers are organized in topical sections on knowledge representation, reasoning about actions, belief revision, theorem proving, argumentation, agents, decidability and complexity, updates, and preferences.

Human beings have been aware of the risks associated with knowledge or its associated technologies since the dawn of time. Not just in Greek mythology, but in the founding myths of Judeo-Christian religions, there are signs and warnings against these dangers. Yet, such warnings and forebodings have never made as much sense as they do today. This stems from the emergence of machines capable of cognitive functions performed exclusively by humans until recently. Besides those technical problems associated with its design and conceptualization, the cognitive revolution, brought about by the development of AI also gives rise to social and economic problems that directly impact humanity. Therefore, it is vital and urgent to examine AI from a moral point of view. The moral problems are two-fold: on the one hand, those associated with the type of society we wish to promote through automation, complexification and power of data processing available today; on the other, how to program decision-making machines according to moral principles acceptable to those humans who will share knowledge and action with them.

Source: "This book addresses the fundamentals of machine ethics. It discusses abilities required for ethical machine reasoning and the programming features that enable them. It connects ethics, psychological ethical processes, and machine implemented procedures. From a technical point of view, the book uses logic programming and evolutionary game theory to model and link the individual and collective moral realms. It also reports on the results of experiments performed using several model implementations. Opening specific and promising inroads into the terra incognita of machine ethics, the authors define here new tools and describe a variety of program-tested moral applications and implemented systems. In addition, they provide alternative readings paths, allowing readers to best focus on their specific interests and to explore the concepts at different levels of detail. Mainly written for researchers in cognitive science, artificial intelligence, robotics, philosophy of technology and engineering of ethics, the book will also be of general interest to other academics, undergraduates in search of research topics, science journalists as well as science and society forums, legislators and military organizations concerned with machine ethics."