Abduction as the Mother of All Argumentation

Abduction* is the genus with deduction and induction as species. Modus tollens is backward reasoning as an unknown proposition is inferred from a known proposition. Reductio ad absurdum is abductive because the conclusion is inferred by deriving a contradiction from an assumption. Inductive reasoning from effect to cause is also backward reasoning. But abduction* consists of forward reasoning as well. The generic structure of abductive* argumentation is universal among all cultures, occupations and disciplines

Multi Agent Diagnosis: an analysis

The paper analyzes the use of a Multi Agent System for Model Based Diagnosis. In a large dynamical system, it is often infeasible or even impossible to maintain a model of the whole system. Instead, several incomplete models of the system have to be used to detect possible faults. These models may also be physically be distributed. A Multi Agent System of diagnostic agents may offer solutions for establishing a global diagnosis. If we use a separate agent for each incomplete model of the system, establishing a global diagnosis becomes a problem cooperation and negotiation between the diagnostic agents. This raises the question whether `a set of diagnostic agents, each having an incomplete model of the system, can (efficiently) determine the same global diagnosis as an ideal single diagnostic agent having the combined knowledge of the diagnostic agents?''economics of technology ;

Human-Intelligence and Machine-Intelligence Decision Governance Formal Ontology

Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational systems including healthcare and medical diagnosis, automated stock trading, robotic production, telecommunications, space explorations, and homeland security. Self-driving cars and drones are just the latest extensions of AI. This thrust of AI into organizations and daily life rests on the AI community’s unstated assumption of its ability to completely replicate human learning and intelligence in AI. Unfortunately, even today the AI community is not close to completely coding and emulating human intelligence into machines. Despite the revolution of digital and technology in the applications level, there has been little to no research in addressing the question of decision making governance in human-intelligent and machine-intelligent (HI-MI) systems. There also exists no foundational, core reference, or domain ontologies for HI-MI decision governance systems. Further, in absence of an expert reference base or body of knowledge (BoK) integrated with an ontological framework, decision makers must rely on best practices or standards that differ from organization to organization and government to government, contributing to systems failure in complex mission critical situations. It is still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence and machine-intelligence (HI-MI) decision capacity is required in any given decision situation. To address this deficiency, this research establishes a formal, top level foundational ontology of HI-MI decision governance in parallel with a grounded theory based body of knowledge which forms the theoretical foundation of a systemic HI-MI decision governance framework

The Reliability of Memory: An Argument from the Armchair

The “problem of memory” in epistemology is concerned with whether and how we could have knowledge, or at least justification, for trusting our apparent memories. I defend an inductive solution—more precisely, an abductive solution—to the problem. A natural worry is that any such solution would be circular, for it would have to depend on memory. I argue that belief in the reliability of memory can be justified from the armchair, without relying on memory. The justification is, roughly, that my having the sort of experience that my apparent memory should lead me to expect is best explained by the hypothesis that my memories are reliable. My solution is inspired by Harrod’s (1942) inductive solution. Coburn (1960) argued that Harrod’s solution contains a fatal flaw. I show that my solution is not vulnerable to Coburn’s objection, and respond to a number of other, recent and likely objections

Free Will, Self‐Creation, and the Paradox of Moral Luck

How is the problem of free will related to the problem of moral luck? In this essay, I answer that question and outline a new solution to the paradox of moral luck, the source-paradox solution. This solution both explains why the paradox arises and why moral luck does not exist. To make my case, I highlight a few key connections between the paradox of moral luck and two related problems, namely the problem of free will and determinism and the paradox of self-creation. Piecing together intuitions, arguments, and insights from recent work on each of these three problems, I argue that the type of control necessary for moral responsibility can only be satisfied by someone who is a genuine source of his own actions, but the relevant notion of sourcehood admits no coherent characterization. If our commonsense view of moral responsibility is incoherent, it is unsurprising that our commitment to the existence of morally responsible agents commits us to some paradoxical things—e.g. to both the existence and impossibility of moral luck

Inferential seemings and the problem of reflective awareness

Phenomenal conservatism (PC) is the internalist view that non-inferential justification rests on appearances. PC’s advocates have recently argued that seemings are also required to explain inferential justification. The most general and developed view to this effect is Huemer (2016)’s theory of inferential seemings (ToIS). Moretti (2018) has shown that PC is affected by the problem of reflective awareness, which makes PC open to sceptical challenges. In this paper I argue that ToIS is afflicted by a version of the same problem and it is thus hostage to inferential scepticism. I also suggest a possible response on behalf of ToIS’s advocates

Relevance differently affects the truth, acceptability, and probability evaluations of “and”, “but”, “therefore”, and “if–then”

In this study we investigate the influence of reason-relation readings of indicative conditionals and ‘and’/‘but’/‘therefore’ sentences on various cognitive assessments. According to the Frege-Grice tradition, a dissociation is expected. Specifically, differences in the reason-relation reading of these sentences should affect participants’ evaluations of their acceptability but not of their truth value. In two experiments we tested this assumption by introducing a relevance manipulation into the truth-table task as well as in other tasks assessing the participants’ acceptability and probability evaluations. Across the two experiments a strong dissociation was found. The reason-relation reading of all four sentences strongly affected their probability and acceptability evaluations, but hardly affected their respective truth evaluations. Implications of this result for recent work on indicative conditionals are discussed

Big data and the SP theory of intelligence

This article is about how the "SP theory of intelligence" and its realisation in the "SP machine" may, with advantage, be applied to the management and analysis of big data. The SP system -- introduced in the article and fully described elsewhere -- may help to overcome the problem of variety in big data: it has potential as "a universal framework for the representation and processing of diverse kinds of knowledge" (UFK), helping to reduce the diversity of formalisms and formats for knowledge and the different ways in which they are processed. It has strengths in the unsupervised learning or discovery of structure in data, in pattern recognition, in the parsing and production of natural language, in several kinds of reasoning, and more. It lends itself to the analysis of streaming data, helping to overcome the problem of velocity in big data. Central in the workings of the system is lossless compression of information: making big data smaller and reducing problems of storage and management. There is potential for substantial economies in the transmission of data, for big cuts in the use of energy in computing, for faster processing, and for smaller and lighter computers. The system provides a handle on the problem of veracity in big data, with potential to assist in the management of errors and uncertainties in data. It lends itself to the visualisation of knowledge structures and inferential processes. A high-parallel, open-source version of the SP machine would provide a means for researchers everywhere to explore what can be done with the system and to create new versions of it.Comment: Accepted for publication in IEEE Acces