Paul Bello

Ability, Breadth, and Parsimony in Computational Models of Higher-Order Cognition

Computational models will play an important role in our understanding of human higher-order cognition. How can a models contribution to this goal be evaluated? This article argues that three important aspects of a model of higher-order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern-day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher-order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a models ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model.

An Architecture for Adaptive Algorithmic Hybrids

We describe a cognitive architecture for creating more robust intelligent systems. Our approach is to enable hybrids of algorithms based on different computational formalisms to be executed. The architecture is motivated by some features of human cognitive architecture and the following beliefs: 1) Most existing computational methods often exhibit some of the characteristics desired of intelligent systems at the cost of other desired characteristics and 2) a system exhibiting robust intelligence can be designed by implementing hybrids of these computational methods. The main obstacle to this approach is that the various relevant computational methods are based on data structures and algorithms that are difficult to integrate into one system. We describe a new method of executing hybrids of algorithms using the focus of attention of multiple modules. The key to this approach is the following two principles: 1) Algorithms based on very different computational frameworks (e.g., logical reasoning, probabilistic inference, and case-based reasoning) can be implemented using the same set of five common functions and 2) each of these common functions can be executed using multiple data structures and algorithms. This approach has been embodied in the Polyscheme cognitive architecture. Systems based on Polyscheme in planning, spatial reasoning, robotics, and information retrieval illustrate that this approach to hybridizing algorithms enables qualitative and measurable quantitative advances in the abilities of intelligent systems.

Belief in The Singularity is Fideistic

We deploy a framework for classifying the bases for belief in a category of events marked by being at once weighty, unseen, and temporally removed (wutr, for short). While the primary source of wutr events in Occidental philosophy is the list of miracle claims of credal Christianity, we apply the framework to belief in The Singularity, surely—whether or not religious in nature—a wutr event. We conclude from this application, and the failure of fit with both rationalist and empiricist argument schemas in support of this belief, not that The Singularity won’t come to pass, but rather that regardless of what the future holds, believers in the “machine intelligence explosion” are simply fideists. While it’s true that fideists have been taken seriously in the realm of religion (e.g. Kierkegaard in the case of some quarters of Christendom), even in that domain the likes of orthodox believers like Descartes, Pascal, Leibniz, and Paley find fideism to be little more than wishful, irrational thinking—and at any rate it’s rather doubtful that fideists should be taken seriously in the realm of science and engineering.

The mental possible worlds mechanism and the lobster problem: an analysis of a complex GRE logical reasoning task

An attempt is made to provide a new psychological mechanism, the mental possible worlds mechanism (MPWM), for analysing complex reasoning tasks such as the logical reasoning tasks in the Graduate Record Examination (GRE). MPWM captures the interaction between syntactic and semantic processes in reasoning, and so it also technically supports the new mental metalogic theory which studies the bridging relations between two major competing theories in the field, mental logic and mental models, whose accounts are also discussed. An empirical study of MPWM is also given.

There Is No Agency Without Attention

ences of the Artificial with a parable about an ant making her way home across a beach and comments that the complexity of her route is not a manifestation of any complex, goal-seeking behavior within the ant but rather reflects the complexity of the environment. The ant “must adapt [her] course repeatedly to the difficulties [she] encounters and often detour uncrossable barriers.” The question that interests us is not whether the ant is complex, but whether she should be considered an intelligent agent. The ant appears to have a goal to get home, and she exhibits control in her ability to navigate novel and complex terrain. Her control is grounded in a coupling of her perceptions to her actions that keep her from continually bumping into an obstacle. Nevertheless, calling the ant an intelligent agent seems like overzealous anthropomorphism on our part. Why is that? Within the artificial intelligence (AI) community, this question is not new. For decades AI researchers have built agents that are capable of carrying out tasks that require human-level or humanlike intelligence. During this time, questions of how these programs compared in kind to humans have surfaced and led to beneficial interdisciplinary Articles

Mechanizing modal psychology

Machines are becoming more capable of substantively interacting with human beings as part of simple dyads and within the confines of our complex social structures. Thought must be given to how their behavior might be regulated with respect to the norms and conventions by which we live. This is certainly true for the military domain [1], but is no less true for eldercare, health care, disaster relief and law enforcement; all areas where robotic systems are poised to make tremendous impact in the near future. But how should we inculcate sensitivity to normative considerations in the next generation of intelligent systems? I argue here for an approach to building moral machines grounded in cognitive architectural considerations, and specifically in the dynamics of how alternatives are represented and reasoned over. After examining some recent results in the empirical literature on human moral judgment, I suggest some desiderata for knowledge representation and reasoning tools that may offer the means to capture some of the foundations of human moral cognition.

Reports of the AAAI 2008 Fall Symposia

The Association for the Advancement of Artificial Intelligence was pleased to present the 2008 Fall Symposium Series, held Friday through Sunday, November 7-9, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia were (1) Adaptive Agents in Cultural Contexts, (2) AI in Eldercare: New Solutions to Old Problems, (3) Automated Scientific Discovery, (4) Biologically Inspired Cognitive Architectures, (5) Education Informatics: Steps toward the International Internet Classroom, (6) Multimedia Information Extraction, and (7) Naturally Inspired AI.