Explaining Emotions

Emotions and cognition are inextricably intertwined. Feelings influence thoughts and actions, which in turn can give rise to new emotional reactions. We claim that people infer emotional states in others using commonsense psychological theories of the interactions among emotions, cognition, and action. We present a situation calculus theory of emotion elicitation representing knowledge underlying commonsense causal reasoning involving emotions, and show how the theory can be used to construct explanations for emotional states. The method for constructing explanations is based on the notion of abduction. This method has been implemented in a computer program called AbMaL. The results of computational experiments using AbMaL to construct explanations of examples based on cases taken from a diary study of emotions indicate that the abductive approach to explanatory reasoning about emotions offers significant advantages. We found that the majority of the diary study examples cannot be explained using deduction alone, but they can be explained by making abjuctive inferences. These inferences provide useful information relevant to emotional states.     

Developmental Analysis of Understanding Language About Quantities and of Solving Problems

We present information-processing models of different levels of knowledge for understanding the language used in texts of arithmetic word Problems, for forming semantic models of the situations that the texts describe, and for making the inferences needed to answer the questions in the problems. In the simplest cognitive models, inferences are limited to properties of sets that exist in a semantic model. In more complex cognitive models, relations between sets are represented internally and support more complex reasoning. Performance on three sets of problems by kindergarten through third-grade students was used to test the models. Global tests provided support for the models. These included measures of scalability and frequencies of individual children's patterns of solutions that agreed with predictions of the models. Performance on problems involving combinations and changes of sets was explained better by the cognitive models than performance on problems involving comparisons. Comparisons may require more advanced understanding of numbers as values of operators rather than only as cardinalities of sets.     

Representing,Running, and Revising Mental Models: A Computational Model

People use commonsense science knowledge to flexibly explain, predict, and manipulate the world around them, yet we lack computational models of how this commonsense science knowledge is represented, acquired, utilized, and revised. This is an important challenge for cognitive science: Building higher order computational models in this area will help characterize one of the hallmarks of human reasoning, and it will allow us to build more robust reasoning systems. This paper presents a novel assembled coherence (AC) theory of human conceptual change, whereby people revise beliefs and mental models by constructing and evaluating explanations using fragmentary, globally inconsistent knowledge. We implement AC theory with Timber , a computational model of conceptual change that revises its beliefs and generates human‐like explanations in commonsense science. Timber represents domain knowledge using predicate calculus and qualitative model fragments, and uses an abductive model formulation algorithm to construct competing explanations for phenomena. Timber then (a) scores competing explanations with respect to previously accepted beliefs, using a cost function based on simplicity and credibility, (b) identifies a low‐cost, preferred explanation and accepts its constituent beliefs, and then (c) greedily alters previous explanation preferences to reduce global cost and thereby revise beliefs. Consistency is a soft constraint in Timber ; it is biased to select explanations that share consistent beliefs, assumptions, and causal structure with its other, preferred explanations. In this paper, we use Timber to simulate the belief changes of students during clinical interviews about how the seasons change. We show that Timber produces and revises a sequence of explanations similar to those of the students, which supports the psychological plausibility of AC theory.     

Second-Order Quantifier Elimination in Higher-Order Contexts with Applications to the Semantical Analysis of Conditionals

Second-order quantifier elimination in the context of classical logic emerged as a powerful technique in many applications, including the correspondence theory, relational databases, deductive and knowledge databases, knowledge representation, commonsense reasoning and approximate reasoning. In the current paper we first generalize the result of Nonnengart and Szałas [17] by allowing second-order variables to appear within higher-order contexts. Then we focus on a semantical analysis of conditionals, using the introduced technique and Gabbay’s semantics provided in [10] and substantially using a third-order accessibility relation. The analysis is done via finding correspondences between axioms involving conditionals and properties of the underlying third-order relation. Presented by Wojciech Buszkowski     

Beyond subgoaling: A dynamic knowledge generation framework for creative problem solving in cognitive architectures

In this paper we propose a computational framework aimed at extending the problem solving capabilities of cognitive artificial agents through the introduction of a novel, goal-directed, dynamic knowledge generation mechanism obtained via a non monotonic reasoning procedure. In particular, the proposed framework relies on the assumption that certain classes of problems cannot be solved by simply learning or injecting new external knowledge in the declarative memory of a cognitive artificial agent but, on the other hand, require a mechanism for the automatic and creative re-framing, or re-formulation, of the available knowledge. We show how such mechanism can be obtained trough a framework of dynamic knowledge generation that is able to tackle the problem of commonsense concept combination. In addition, we show how such a framework can be employed in the field of cognitive architectures in order to overcome situations like the impasse in SOAR by extending the possible options of its subgoaling procedures.     

A Completeness Proof for a Logic with an Alternative Necessity Operator

We show the completeness of a Hilbert-style system LK defined by M. Valiev involving the knowledge operator K dedicated to the reasoning with incomplete information. The completeness proof uses a variant of Makinson's canonical model construction. Furthermore we prove that the theoremhood problem for LK is co-NP-complete, using techniques similar to those used to prove that the satisfiability problem for propositional S5 is NP-complete.     

A NEW LOOK AT THE SCIENCE‐AND‐RELIGION DIALOGUE

Abstract. Cognitive science is beginning to make a contribution to the science‐and‐religion dialogue by its claims about the nature of both scientific and religious knowledge and the practices such knowledge informs. Of particular importance is the distinction between folk knowledge and abstract theoretical knowledge leading to a distinction between folk science and folk religion on the one hand and the reflective, theoretical, abstract form of thought that characterizes both advanced scientific thought and sophisticated theological reasoning on the other. Both folk science and folk religion emerge from commonsense reasoning about the world, a form of reasoning bequeathed to us by the processes of natural selection. Suggestions are made about what scientists and theologians can do if they accept these claims.     

Finding a home: Developmental and neurobiological perspectives on cultural models

This article looks at cultural models in the light of human development, and neurobiological findings in motivation, learning, and cognition. It is argued that at the individual level, the acquisition of cultural models relies on several innate, neurobiologically based motivational, learning, and cognitive systems. These are: (a) a primary motivation to form social bonds which is driven by affect; (b) highly specialized social learning circuits, involving, but not limited to, mirror neuron systems, that facilitate the encoding of social information through implicit, embodied, imitational learning processes; and (c) the formation of culturally based templates for behavior and cognition centered around structures, collectively known as the “default mode network,” which is essential to self‐understanding, autobiographical memory, social cognition, prospection, and theory‐of‐mind. Cultural models, it is argued, are acquired through innate motivational processes that tie the individual emotionally to a secure base of familiar people and customs. This instinctual desire for proximity to others facilitates the efficient, largely implicit, patterning of knowledge and expectations. Shared knowledge and expectations, in turn, create a common, mostly implicit or unconscious, experience of subjectivity within groups. This allows each individual to automatically and effortlessly interact with similarly enculturated others.     

Hypothesizing about signaling networks

The current knowledge about signaling networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. The revision and/or extension is first formulated as theoretical hypotheses, then verified experimentally. Many computer-aided systems have been developed to assist biologists in undertaking this challenge. The majority of the systems help in finding “patterns” in data and leave the reasoning to biologists. A few systems have tried to automate the reasoning process of hypothesis formation. These systems generate hypotheses from a knowledge base and given observations. A main drawback of these knowledge-based systems is the knowledge representation formalism they use. These formalisms are mostly monotonic and are now known to be not quite suitable for knowledge representation, especially in dealing with the inherently incomplete knowledge about signaling networks. We propose an action language based framework for hypothesis formation for signaling networks. We show that the hypothesis formation problem can be translated into an abduction problem. This translation facilitates the complexity analysis and an efficient implementation of our system. We illustrate the applicability of our system with an example of hypothesis formation in the signaling network of the p53 protein.     

Combined reasoning by automated cooperation

Different reasoning systems have different strengths and weaknesses, and often it is useful to combine these systems to gain as much as possible from their strengths and retain as little as possible from their weaknesses. Of particular interest is the integration of first-order and higher-order techniques. First-order reasoning systems, on the one hand, have reached considerable strength in some niches, but in many areas of mathematics they still cannot reliably solve relatively simple problems, for example, when reasoning about sets, relations, or functions. Higher-order reasoning systems, on the other hand, can solve problems of this kind automatically. But the complexity inherent in their calculi prevents them from solving a whole range of problems. However, while many problems cannot be solved by any one system alone, they can be solved by a combination of these systems.We present a general agent-based methodology for integrating different reasoning systems. It provides a generic integration framework which facilitates the cooperation between diverse reasoners, but can also be refined to enable more efficient, specialist integrations. We empirically evaluate its usefulness, effectiveness and efficiency by case studies involving the integration of first-order and higher-order automated theorem provers, computer algebra systems, and model generators.     

Beyond belief bias: Reasoning from conceptual structures by mental models manipulation

Mental models constitute an alternative to the rule-based systems in the explanation of human reasoning (Johnson-Laird, 1983). In this paper, we claim that the concept of believability generally used to categorize content and context effects is of little use within a semantic theory. Thus, we propose the use of categories that are directly extracted from subjective relations among concepts within the reasoning problem. We demonstrate that manipulations based on this kind of categorization produce predictable patterns of responses in reasoning problems. We present two experiments to test our predictions, using conditional and syllogistic reasoning problems, and in both cases, we demonstrate the influence of conceptual knowledge not only in natural contexts, but also in experimentally created artificial contexts.     

Who Reasons Well? Two Studies of Informal Reasoning Among Children of Different Grade,Ability, and Knowledge Levels

This article presents the results of two experiments addressing the relation of reasoning skill to student grade, ability, and knowledge levels. In the first experiment, three levels of students within each of four grades-5, 7, 9, or 1 1-were designated as intellectually gifted, average, or below average. They were given three tasks involving everyday problems for which they provided solutions and justifications. The second experiment included the measurement of domain knowledge with grade and ability level. Measures of informal reasoning showed a substantial relation between ability level and performance, with knowledge significantly related to performance measures, such as number and type of reasons generated, but not to measures involving soundness or acceptability of arguments, which were explained by ability level. Grade was related only to an increase in personal and broadly defined social reasons; other effects were "washed out" by knowledge. The findings were interpreted in terms of a two-component model of informal reasoning, a knowledgeexperiential component and an informal reasoning skill component based on the acquisition of argumentation-based language structures termed conventions of reasoning. Consideration of the relation of reasoning to learning and to instruction emphasized the importance of teaching informal reasoning skill.     

Plausible Reasoning and the Resolution of Quantifier Scope Ambiguities

Despite overwhelming evidence suggesting that quantifier scope is a phenomenon that must be treated at the pragmatic level, most computational treatments of scope ambiguities have thus far been a collection of syntactically motivated preference rules. This might be in part due to the prevailing wisdom that a commonsense inferencing strategy would require the storage of and reasoning with a vast amount of background knowledge. In this paper we hope to demonstrate that the challenge in developing a commonsense inferencing strategy is in the discovery of the relevant commonsense data and in a proper formulation of the inferencing strategy itself, and that a massive amount of background knowledge is not always required. In particular, we present a very effective procedure for resolving quantifier scope ambiguities at the pragmatic level using simple quantitative data that is readily available in most database environments.     

Belief-desire reasoning in the explanation of behavior: do actions speak louder than words?

The mechanisms underwriting our commonsense psychology, or 'theory of mind', have been extensively investigated via reasoning tasks that require participants to predict the action of agents based on information about beliefs and desires. However, relatively few studies have investigated the processes contributing to a central component of 'theory of mind' - our ability to explain the action of agents in terms of underlying beliefs and desires. In two studies, we demonstrate a novel phenomenon in adult belief-desire reasoning, capturing the folk notion that 'actions speak louder than words'. When story characters were described as searching in the wrong place for a target object, adult subjects often endorsed mental state explanations referencing a distracter object, but only when that object was approached. We discuss how this phenomenon, alongside other reasoning "errors" (e.g., hindsight bias; the curse of knowledge) can be used to illuminate the architecture of domain specific belief-desire reasoning processes.     

Towards a Rough Mereology-Based Logic for Approximate Solution Synthesis. Part 1

We are concerned with formal models of reasoning under uncertainty. Many approaches to this problem are known in the literature e.g. Dempster-Shafer theory [29], [42], bayesian-based reasoning [21], [29], belief networks [29], many-valued logics and fuzzy logics [6], non-monotonic logics [29], neural network logics [14]. We propose rough mereology developed by the last two authors [22-25] as a foundation for approximate reasoning about complex objects. Our notion of a complex object includes, among others, proofs understood as schemes constructed in order to support within our knowledge assertions/hypotheses about reality described by our knowledge incompletely.