Why are some models, like the harmonic oscillator, the Ising model, a few Hamiltonian equations in quantum mechanics, the poisson equation, or the Lokta-Volterra equations, repeatedly used within and across scientific domains, whereas theories allow for many more modeling possibilities? Some historians and philosophers of science have already proposed plausible explanations. For example, Kuhn and Cartwright point to a tendency toward conservatism in science, and Humphreys emphasizes the importance of the intractability of what he calls “templates.” This paper investigates more (...) systematically the reasons for this remarkable interdisciplinary recurrence. To this aim, the authors describe in more detail the phenomenon they focus on and review competing potentialexplanations. The authors disentangle the various assumptions underlying these explanations based on sensitivity to a computational constraints and assess its relationships with the other analyzed explanatons. (shrink)

It is argued that the traditional distinction between artificial intelligence and cognitive simulation amounts to little more than a difference in style of research - a different ordering in goal priorities and different methodological allegiances. Both enterprises are constrained by empirical considerations and both are directed at understanding classes of tasks that are defined by essentially psychological criteria. Because of the different ordering of priorities, however, they occasionally take somewhat different stands on such issues as the power/generality trade-off and on (...) the relevance of the sort of data collected in experimental psychology laboratories. (shrink)

Empirical analyses have provided some important constraints for computational theories of metaphor. Three such constraints relate to (1) the similar processing time for literal and metaphorical language, (2) the time‐limited processing of many metaphors, and (3) the dissociation of metaphor comprehension and appreciation. Indurkhya's (1986, 1987) model is discussed with respect to these issues.

Though there is yet no consensus on the right way to understand ‘physicalism’, most philosophers agree that, regardless of whatever else is required, physicalism cannot be true if there exists fundamental mentality. I will follow Jessica Wilson (Philosophical Studies 131:61–99, 2006) in calling this the 'No Fundamental Mentality' (NFM) constraint on physicalism. Unfortunately for those who wish to constrain physicalism in this way, NFM admits of a counterexample: an artificially intelligent quantum computer which employs quantum properties as part of its (...) cognitive operations. If one of these quantum properties serves a proper functional role in the artificial intelligence, then that property counts as a mental under the physicalism-friendly theory of mentality called “realizer functionalism”, which says that a lower-order property is mental if it satisfies an appropriate higher-order functional description. Further, if this quantum property is both fundamental and mental, then NFM must rule that it is not physical. Yet the existence of such an artificially intelligent quantum computer, which possesses mental properties solely in virtue of the functional roles those properties play, is surely consistent with the truth of physicalism. This ought to motivate NFM proponents to reformulate their view. (shrink)

The thesis examines the philosophical implications of the computational theory of early vision developed by Marr. According to Marr, early visual processes consist of sequences of "modular" computational mechanisms. These processes rely on functional relations between rates of change in stimulus magnitudes which result from certain contingent, global properties--natural constraints--of the physical world. ;Marr argues that explanations of early vision must have three distinct levels of description: computational, algorithmic and physical. In Chapter 1 I defend the (...) explanatory significance of this distinction in levels. In fulfilling its role in describing the dependence of visual processes on natural constraints, the computational level forms an autonomous level of description in the sense that it is unaffected by the computational steps at the other levels. ;In Chapter 2 I discuss the implications of natural constraints for the issues of individualism and methodological solipsism. I conclude that Mart's theory is nonindividualistic in the sense that visual content does not supervene on neural properties. However, this merely reflects the fact that different computational theories may be selected for the same system. Importantly, Marr's theory does not violate methodological solipsism since interpretations within theories must supervene on neural properties. ;In Chapter 3 I argue from the results of Chapters 1 and 2 that psychological explanation does not reduce to neurophysiology. This conclusion does not follow from the functionalist argument against physicalism, which is based on an incorrect account of computational theories. Rather the conclusion reflects the explanatory incompleteness of neurophysiological theories given the autonomous role of the computational level. ;In Chapter 4 I look in detail at the arguments for a "language of thought" as they apply to early vision. I distinguish two versions of the language of thought hypothesis, one weaker than the other. I conclude that the stronger version, which claims that a cognitive system is "program-using", is false of early vision because of the role of natural constraints. The weaker claim that cognitive processes employ symbolic transformations is true of the computational-level theory of early vision, but there is insufficient evidence to establish the claim at the algorithmic level. (shrink)

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and (...) other tools to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations. (shrink)

This research explores the relationship between the bounded in-degree and out-degree of an argumentation framework and the computational complexity of the problems of Credulous Acceptance ( CredA ) and Skeptical Acceptance ( SkepA ) under preferred extensions. Researchers have studied the complexity of these problems when the in-degree [Formula: see text] and out-degree [Formula: see text] of the arguments are restricted to [Formula: see text]. Despite this restriction, the computational complexities of CredA and SkepA persist. Based on these (...) results, we presents new results that provide deeper insights into the impact of additional constraints on the argumentation framework. Specifically, we show that when “[Formula: see text]” or “[Formula: see text]” is restricted to [Formula: see text], both problems CredA and SkepA can be solved in polynomial time. Subsequently, we impose additional constraints on the argumentation framework by analyzing the quantities “[Formula: see text],” “[Formula: see text],” and “[Formula: see text].” For an argumentation framework [Formula: see text] and all arguments “[Formula: see text]” of [Formula: see text], the parameter “[Formula: see text]” indicates the maximum number of attackers that any argument “[Formula: see text]” can have among all those that are not attacked by “[Formula: see text]” [Formula: see text], “[Formula: see text]” indicates the maximum number of arguments that any argument “[Formula: see text]” attacks among all those that do not attack “[Formula: see text]”, and “[Formula: see text]” denotes the maximum number of arguments that any argument “[Formula: see text]” attacks among those that attack “[Formula: see text]”. Surprisingly, even when all these quantities are restricted to [Formula: see text], the computational complexity of CredA persists. Furthermore, we explore the influence of symmetric attacks by fixing “[Formula: see text]” to zero, while setting “[Formula: see text]” and “[Formula: see text]” to [Formula: see text]. Remarkably, the complexity of CredA persists under this restriction as well. (shrink)

There is substantial variation in language experience, yet there is surprising similarity in the language structure acquired. Constraints on language structure may be external modulators that result in this canalization of language structure, or else they may derive from the broader, communicative environment in which language is acquired. In this paper, the latter perspective is tested for its adequacy in explaining robustness of language learning to environmental variation. A computational model of word learning from cross-situational, multimodal information was (...) constructed and tested. Key to the model's robustness was the presence of multiple, individually unreliable information sources to support learning. This “degeneracy” in the language system has a detrimental effect on learning, compared to a noise-free environment, but has a critically important effect on acquisition of a canalized system that is resistant to environmental noise in communication. (shrink)

A central tenet of cognitive linguistics is that adults’ knowledge of language consists of a structured inventory of constructions, including various two-argument constructions such as the active, the passive and “fronting” constructions. But how do speakers choose which construction to use for a particular utterance, given constraints such as discourse/information structure and the semantic fit between verb and construction? The goal of the present study was to build a computational model of this phenomenon for two-argument constructions in Mandarin. (...) First, we conducted a grammaticality judgment study with 60 native speakers which demonstrated that, across 57 verbs, semantic affectedness – as determined by further 16 native speakers – predicted each verb’s relative acceptability in the bei-passive and ba-active constructions, but not the Notional Passive and SVO Active constructions. Second, in order to simulate acquisition of these competing constraints, we built a computational model that learns to map from corpus-derived input to an output representation corresponding to these four constructions. The model was able to predict judgments of the relative acceptability of the test verbs in the ba-active and bei-passive constructions obtained in Study 1, with model-human correlations in the region of r = 0.5 and r = 0.3, respectively. Surprisingly, these correlations increased when lexical verb identity was removed; perhaps because this information leads to over-fitting of the training set. These findings suggest the intriguing possibility that acquiring constructions involves forgetting as a mechanism for abstracting across certain fine-grained lexical details and idiosyncrasies. (shrink)

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. (shrink)

Computational methods such as computer simulations, Monte Carlo methods, and agent-based modeling have become the dominant techniques in many areas of science. Extending Ourselves contains the first systematic philosophical account of these new methods, and how they require a different approach to scientific method. Paul Humphreys draws a parallel between the ways in which such computational methods have enhanced our abilities to mathematically model the world, and the more familiar ways in which scientific instruments have expanded our access (...) to the empirical world. This expansion forms the basis for a new kind of empiricism, better suited to the needs of science than the older anthropocentric forms of empiricism. Human abilities are no longer the ultimate standard of epistemological correctness.Humphreys also includes arguments for the primacy of properties rather than objects, the need to consider technological constraints when appraising scientific methods, and a detailed account of how the path from computational template to scientific application is constructed. This last feature allows us to hold a form of selective realism in which anti-realist arguments based on formal reconstructions of theories can be avoided. One important consequence of the rise of computational methods is that the traditional organization of the sciences is being replaced by an organization founded on computational templates.Extending Ourselves will be of interest to philosophers of science, epistemologists, and to anyone interested in the role played by computers in modern science. (shrink)

Colonius suggests that, in using standard set theory as the language in which to express our computational-level theory of human memory, we would need to violate the axiom of foundation in order to express meaningful memory bindings in which a context is identical to an item in the list. We circumvent Colonius's objection by allowing that a list item may serve as a label for a context without being identical to that context. This debate serves to highlight the value (...) of specifying memory operations in set theoretic notation, as it would have been difficult if not impossible to formulate such an objection at the algorithmic level. (shrink)

Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement (...) among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations. (shrink)

Most of the IoT-based smart systems require low latency and crisp response time for their applications. Achieving the demand of this high Quality of Service becomes quite challenging when computationally intensive tasks are offloaded to the cloud for execution. Edge computing therein plays an important role by introducing low network latency, quick response, and high bandwidth. However, offloading computations at a large scale overwhelms the edge server with many requests and the scalability issue originates. To address the above issues, an (...) efficient resource management technique is required to maintain the workload over the edge and ensure the reduction of response time for IoT applications. Therefore, in this paper, we introduce a metaheuristic and nature-inspired Artificial Bee Colony optimization technique that effectively manages the workload over the edge server under the strict constraints of low network latency and quick response time. The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization, Ant Colony Optimization, and Round-Robin Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. Furthermore, the proposed technique scales the edge server to meet the demand of high QoS for IoT applications. (shrink)

The ability to reason by analogy is particularly important because it permits the extension of knowledge of a target domain by virtue of its similarity to a base domain via a process of analogical inference. The general procedure for analogical inference involves copying structure from the base to the target in which missing information is generated, and substitutions are made for items for which analogical correspondences have already been found. A pure copying with substitution and generation process is too profligate (...) to be useful, and so constraints must be placed on what information is to be carried over. In this paper, the importance of systematicity as a constraint on inference is explored in four studies in which subjects find correspondences between domains and also make inferences. This work suggests that people prefer to make inferences of information connected to systematic correspondences between domains. A second important theme of this paper is that violations of one‐to‐one mapping can lead to inconsistent object substitutions in inference. The data reveal no such inconsistent substitutions in people's inferences, suggesting that they do respect one‐to‐one mapping in analogical inference. These findings are discussed relative to four prominent computational models of analogical mapping and inference. (shrink)

The article suggests that the best examples of textual work in the computational humanities are best understood as motivated by aesthetic concerns with the constraints placed on literature by computation’s cultural hegemony. To draw these concerns out, I adopt a middle-distant depth of field, examining the strange epistemology and unexpected aesthetic dimension of numerical culture’s encounters with literature. The middle-distant forms of reading I examine register problematically as literary scholarship not because they lack rigor or evidence but because (...) their unacknowledged object of study is the infrastructure of academic knowledge production. Work in the computational humanities is approaching a point at which the scale of analyzed data and data analysis washes out readings, the algorithms are achieving opaque complexity, and the analytical systems are producing purposive outputs. These problems cannot be addressed without attending to the aesthetics of data-driven cultural encounters, specifically the questions of how we produce readings/viewings and how they change our perceptions and characterize the interesting, critical theorization on method and meaning that make the best work in the computational humanities legitimately humanistic. I contribute a working example: a recommendation system for passages within the Shakespearean dramatic corpus, built using a large bibliographical dataset from JSTOR, a counting/ranking algorithm used at large scale. The system returns passages as intertexts for the passage a reader has selected. I explain how and why this system provides meaningful intertextual connections within the Shakespearean dramatic corpus by tracing the legible structural effects of disciplinary knowledge formation on the shape of this dataset. I close by suggesting how the computational and more traditional methods in the humanities might begin to stop debating past one another. (shrink)

Constraint programming is the fruit of several decades of research carried out in mathematical logic, automated deduction, operations research and arti?cial intelligence. The tools and programming languages arising from this research?eldhaveenjoyedrealsuccessintheindustrialworldastheycontributetosolving hard combinatorial problems in diverse domains such as production planning, communication networks, robotics and bioinformatics. This volume contains the extended and reviewed versions of a selection of papers presented at the Joint ERCIM/CoLogNET International Workshop on Constraint Solving and Constraint Logic Programming, which was held from June 30 to July (...) 2, 2003. The venue chosen for the seventh edition of this annual workshop was the Computer and Automation Research Institute of the Hungarian Academy of Sciences in Budapest, Hungary. This institute is one of the 20 members of the Working Group on Constraints of the European Research Consortium for Informatics and Mathematics. For many participants this workshop provided the?rst opportunity to visit their ERCIM partner in Budapest. CoLogNET is the European-funded network of excellence dedicated to s- porting and enhancing cooperation and research on all areas of computational logic, and continues the work done previously by the Compulog Net. In part- ular, the aim of the logic and constraint logic programming area of CoLogNET is to foster and support all research activities related to logic programming and constraint logic programming. The editors would like to take the opportunity and thank all the authors who submitted papers to this volume, as well as the reviewers for their helpful work. (shrink)

In this commentary, I advocate for strict relations between Marr's levels of analysis. Under a strict relationship, each level is exactly implemented by the subordinate level. This yields two benefits. First, it brings consistency for multilevel explanations. Second, similar to how a sculptor chisels away superfluous marble, a modeler can chisel a computational-level model by applying constraints. By sculpting the model, one restricts the set of possible algorithmic- and implementational-level theories.

Neurophysiological investigations of the visual system by way of single-cell recordings have revealed a hierarchical architecture in which lower level areas, such as the primary visual cortex, contain cells that respond to simple features, while higher level areas contain cells that respond to higher order features apparently composed of combinations of lower level features. This architecture seems to suggest a feed-forward processing strategy in which visual information progresses from lower to higher visual areas. However there is other evidence, both neurophysiological (...) and phenomenal, that suggests a more parallel processing strategy in biological vision, in which top-down feedback plays a significant role. In fact Gestalt theory suggests that visual perception involves a process of emergence, i.e. a dynamic relaxation of multiple constraints throughout the system simultaneously, so that the final percept represents a stable state, or energy minimum of the dynamic system as a whole. A Multi-Level Reciprocal Feedback (MLRF) model is proposed to resolve the apparently contradictory concepts, by proposing a hierarchical visual architecture whose different levels are connected by bi-directional feed-forward and feedback pathways, where the computational transformation performed by the feedback pathway between levels in the hiararchy is a kind of inverse of the transformation performed by the corresponding feed-forward processing stream. This alternative paradigm of perceptual computation accounts in general terms for a number of visual illusory effects, and offers a computational specification for the generative, or constructive aspect of perceptual processing revealed by Gestalt theory. (shrink)

Many recent studies of dynamics in formal argumentation within AI focus on the well-known formalism of Dung’s argumentation frameworks (AFs). Despite the usefulness of AFs in many areas of argumentation, their abstract notion of arguments creates a barrier for operators that modify a given AF, e.g., in the case that dependencies between arguments have been abstracted away that are important for subsequent modifications. In this paper we aim to support development of dynamic operators on formal models in abstract argumentation by (...) providing constraints imposed on the modification of the structure that can be used to incorporate information that has been abstracted away. Towards a broad reach, we base our results on the general formalism of abstract dialectical frameworks (ADFs) in abstract argumentation. To show applicability, we present two cases studies that adapt an existing extension enforcement operator that modifies AFs: in the first case study, we show how to utilize constraints in order to obtain an enforcement operator on ADFs that is allowed to only add support relations between arguments, and in the second case study we show how an enforcement operator on AFs can be defined that respects dependencies between arguments. We show feasibility of our approach by studying the complexity of the proposed structural constraints and the operators arising from the case studies, and by an experimental evaluation of an answer set programming (ASP) implementation of the enforcement operator based on supports. (shrink)

This paper provides a computational characterization of coherence that applies to a wide range of philosophical problems and psychological phenomena. Maximizing coherence is a matter of maximizing satisfaction of a set of positive and negative constraints. After comparing five algorithms for maximizing coherence, we show how our characterization of coherence overcomes traditional philosophical objections about circularity and truth.

A theory of analogical mapping between source and target analogs based upon interacting structural, semantic, and pragmatic constraints is proposed here. The structural constraint of isomorphism encourages mappings that maximize the consistency of relational corresondences between the elements of the two analogs. The constraint of semantic similarity supports mapping hypotheses to the degree that mapped predicates have similar meanings. The constraint of pragmatic centrality favors mappings involving elements the analogist believes to be important in order to achieve the purpose (...) for which the analogy is being used. The theory is implemented in a computer program called ACME (Analogical Constraint Mapping Engine), which represents constraints by means of a network of supporting and competing hypotheses regarding what elements to map. A cooperative algorithm for parallel constraint satisfaction identities mapping hypotheses that collectively represent the overall mapping that best fits the interacting constraints. ACME has been applied to a wide range of examples that include problem analogies, analogical arguments, explanatory analogies, story analogies, formal analogies, and metaphors. ACME is sensitive to semantic and pragmatic information if it is available, and yet able to compute mappings between formally isomorphic analogs without any similar or identical elements. The theory is able to account for empirical findings regarding the impact of consistency and similarity on human processing of analogies. (shrink)

The SRL of King is a sound, complete and decidable logic designed specifically to support formalisms for the HPSG of Pollard and Sag. The SRL notion of modellability in a signature is particularly important for HPSG, and the present paper modifies an elegant method due to Blackburn and Spaan in order to prove that – modellability in each computable signature is 1 0 – modellability in some finite signature is 1 0 -hard, and – modellability in some finite signature is (...) decidable. (shrink)

Domain constraint, the requirement that analogues be selected from "the same category," inheres in the popular saying "you can't compare apples and oranges" and the textbook principle "the greater the number of shared properties, the stronger the argument from analogy." I identify roles of domains in biological, linguistic, and legal analogy, supporting the account of law with a computer word search of judicial decisions. I argue that the category treatments within these disciplines cannot be exported to general informal logic, where (...) the relevance of properties, not their number, must be the logically prior criterion for evaluating analogical arguments. (shrink)

Do computers have beliefs? I argue that anyone who answers in the affirmative holds a view that is incompatible with what I shall call the commonsense approach to the propositional attitudes. My claims shall be two. First,the commonsense view places important constraints on what can be acknowledged as a case of having a belief. Second, computers – at least those for which having a belief would be conceived as having a sentence in a belief box – fail to satisfy (...) some of these constraints. This second claim can best be brought out in the context of an examination of the idea of computer self-knowledge and self-deception, but the conclusion is perfectly general: the idea that computers are believers, like the idea that computers could have self-knowledge or be self-deceived, is incompatible with the commonsense view. The significance of the argument lies in the choice it forces on us: whether to revise our notion of belief so as to accommodate the claim that computers are believers, or to give up on that claim so as to preserve our pretheoretic notion of the attitudes. We cannot have it both ways. (shrink)

The topology optimization of computer communication network is studied based on improved genetic algorithm, a network optimization design model based on the establishment of network reliability maximization under given cost constraints, and the corresponding improved GA is proposed. In this method, the corresponding computer communication network cost model and computer communication network reliability model are established through a specific project, and the genetic intelligence algorithm is used to solve the cost model and computer communication network reliability model, respectively. It (...) has been proved that GA can solve the complex problems of computer working environment better, which is 80% higher than the general algorithm, and can select the optimal scheme pertinently. (shrink)

This volume contains the proceedings of three special sessions: Algebra and Computer Science, held during the Joint AMS-EMS-SPM meeting in Porto, Portugal, June 10–13, 2015; Groups, Algorithms, and Cryptography, held during the Joint Mathematics Meeting in San Antonio, TX, January 10–13, 2015; and Applications of Algebra to Cryptography, held during the Joint AMS-Israel Mathematical Union meeting in Tel-Aviv, Israel, June 16–19, 2014. Papers contained in this volume address a wide range of topics, from theoretical aspects of algebra, namely group theory, (...) universal algebra and related areas, to applications in several different areas of computer science. From the computational side, the book aims to reflect the rapidly emerging area of algorithmic problems in algebra, their computational complexity and applications, including information security, constraint satisfaction problems, and decision theory. The book gives special attention to recent advances in quantum computing that highlight the need for a variety of new intractability assumptions and have resulted in a new area called group-based cryptography. (shrink)

Triviality arguments against the computational theory of mind claim that computational implementation is trivial and thus does not serve as an adequate metaphysical basis for mental states. It is common to take computational implementation to consist in a mapping from physical states to abstract computational states. In this paper, I propose a novel constraint on the kinds of physical states that can implement computational states, which helps to specify what it is for two physical states (...) to non-trivially implement the same computational state. (shrink)

This paper describes a theory that explains both the creativity and the efficiency of people's conceptual combination. In the constraint theory, conceptual combination is controlled by three constraints of diagnosticity, plausibility, and informativeness. The constraints derive from the pragmatics of communication as applied to compound phrases. The creativity of combination arises because the constraints can be satisfied in many different ways. The constraint theory yields an algorithmic model of the efficiency of combination. The C3 model admits the (...) full creativity of combination and yet efficiently settles on the best interpretation for a given phrase. The constraint theory explains many empirical regularities in conceptual combination, and makes various empirically verified predictions. In computer simulations of compound phrase interpretation, the C3 model has produced results in general agreement with people's responses to the same phrases. (shrink)

This paper reports a computer program to generate novel designs for the arrangement of furniture within a simulated room. It is based on the engagement-reflection computer model of the creative processes. During engagement the system generates material in the form of sequences of actions (e.g. change the colours of the walls, include some furniture in the room, modify their colour, and so on) guided by content and knowledge constraints. During reflection, the system evaluates the composition produced so far and, (...) if it is necessary, modifies it. We discuss the implementation of the system and some of its most salient features, especially the use of a computational model for creativity in the terrain of design. We argue that this kind of model opens new possibilities for the simulation of the design processes as well as the development of tools. (shrink)

Complex organisms thwart the simple rectilinear causality paradigm of “necessary and sufficient,” with its experimental strategy of “knock down and overexpress.” This Essay organizes the eccentricities of biology into four categories that call for new mathematical approaches; recaps for the biologist the philosopher's recent refinements to the causation concept and the mathematician's computational tools that handle some but not all of the biological eccentricities; and describes overlooked insights that make causal properties of physical hierarchies such as emergence and downward (...) causation straightforward. Reviewing and extrapolating from similar situations in physics, it is suggested that new mathematical tools for causation analysis incorporating feedback, signal cancellation, nonlinear dependencies, physical hierarchies, and fixed constraints rather than instigative changes will reveal unconventional biological behaviors. These include “eigenisms,” organisms that are limited to quantized states; trajectories that steer a system such as an evolving species toward optimal states; and medical control via distributed “sheets” rather than single control points. (shrink)

``Neural computing'' is a research field based on perceiving the human brain as an information system. This system reads its input continuously via the different senses, encodes data into various biophysical variables such as membrane potentials or neural firing rates, stores information using different kinds of memories (e.g., short-term memory, long-term memory, associative memory), performs some operations called ``computation'', and outputs onto various channels, including motor control commands, decisions, thoughts, and feelings. We show a natural model of neural computing that (...) gives rise to hyper-computation. Rigorous mathematical analysis is applied, explicating our model's exact computational power and how it changes with the change of parameters. Our analog neural network allows for supra-Turing power while keeping track of computational constraints, and thus embeds a possible answer to the superiority of the biological intelligence within the framework of classical computer science. We further propose it as standard in the field of analog computation, functioning in a role similar to that of the universal Turing machine in digital computation. In particular an analog of the Church-Turing thesis of digital computation is stated where the neural network takes place of the Turing machine. (shrink)

Language and culture endow humans with access to conceptual information that far exceeds any which could be accessed by a non-human animal. Yet, it is possible that, even without language or specific experiences, non-human animals represent and infer some aspects of similarity relations between objects in the same way as humans. Here, we show that monkeys’ discrimination sensitivity when identifying images of animals is predicted by established measures of semantic similarity derived from human conceptual judgments. We used metrics from computer (...) vision and computational neuroscience to show that monkeys’ and humans’ performance cannot be explained by low-level visual similarity alone. The results demonstrate that at least some of the underlying structure of object representations in humans is shared with non-human primates, at an abstract level that extends beyond low-level visual similarity. Because the monkeys had no experience with the objects we tested, the results suggest that monkeys and humans share a primitive representation of object similarity that is independent of formal knowledge and cultural experience, and likely derived from common evolutionary constraints on object representation. (shrink)

Much research on cognitive development focuses either on early-emerging domain-specific knowledge or domain-general learning mechanisms. However, little research examines how these sources of knowledge interact. Previous research suggests that young infants can make inferences from samples to populations (Xu & Garcia, 2008) and 11- to 12.5-month-old infants can integrate psychological and physical knowledge in probabilistic reasoning (Teglas, Girotto, Gonzalez, & Bonatti, 2007; Xu & Denison, 2009). Here, we ask whether infants can integrate a physical constraint of immobility into a statistical (...) inference mechanism. Results from three experiments suggest that, first, infants were able to use domain-specific knowledge to override statistical information, reasoning that sometimes a physical constraint is more informative than probabilistic information. Second, we provide the first evidence that infants are capable of applying domain-specific knowledge in probabilistic reasoning by using a physical constraint to exclude one set of objects while computing probabilities over the remaining sets. (shrink)

Computer and information ethics, as well as other fields of applied ethics, need ethical theories which coherently unify deontological and consequentialist aspects of ethical analysis. The proposed theory of just consequentialism emphasizes consequences of policies within the constraints of justice. This makes just consequentialism a practical and theoretically sound approach to ethical problems of computer and information ethics.