Perception-based Information Research Articles

Towards a Computational Theory of Perceptions under Neutrosophic Environment

Computing and reasoning with perceptions in AI are shifting from traditional methods to those that mimic human cognitive processes. Humans intuitively measure and evaluate without explicit computations, using sensory input and past experiences for quick judgments. The theory of perceptions can enhance AI's ability to replicate human-like assessment by providing a framework for handling the nuances of human reasoning. Traditionally, information granularity is either crisp (c-granular) or fuzzy (f-granular), focusing on measurement-based and perception-based information. This paper outlines a neutrosophic-based computational theory of perceptions (n-CTP), introducing neutrosophic granular (n-granular) information and neutrosophic constraint-centered semantics of natural languages (n-CSNL). Based on the neutrosophy theory, which deals with truth, falsity, and indeterminacy, managing uncertainties and vagueness in real-world data can be done more effectively than with traditional methods. The n-CTP has the potential to enhance AI's ability to process and reason with complex, uncertain, and ambiguous information.

Factors associated with poor perceptions of the COVID-19 pandemic in Africa

This study examines factors associated with an increasingly poor perception of the novel coronavirus in Africa using a designed electronic questionnaire to collect perception-based information from participants across Africa from twenty-one African countries (and from all five regions of Africa) between 1 and 25 February 2022. The study received 66.7% of responses from West Africa, 12.7% from Central Africa, 4.6% from Southern Africa, 15% from East Africa, and 1% from North Africa. The majority of the participants are Nigerians (56%), 14.1% are Cameroonians, 8.7% are Ghanaians, 9.3% are Kenyans, 2% are South Africans, 2.1% are DR-Congolese, 1.6% are Tanzanians, 1.2% are Rwandans, 0.4% are Burundians, and others are Botswana’s, Chadians, Comoros, Congolese, Gambians, Malawians, South Sudanese, Sierra Leoneans, Ugandans, Zambians, and Zimbabweans. All responses were coded on a five-point Likert scale. The study adopts descriptive statistics, principal component analysis, and binary logistic regression analysis for the data analysis. The descriptive analysis of the study shows that the level of ignorance or poor “perception” of COVID-19 in Africa is very high (87% of individuals sampled). It leads to skepticism towards complying with preventive measures as advised by the WHO and directed by the national government across Africa. We adopted logistic regression analysis to identify the factors associated with a poor perception of the virus in Africa. The study finds that religion (belief or faith) and media misinformation are the two leading significant causes of ignorance or poor “perception” of COVID-19 in Africa, with log odd of 0.4775 (resulting in 1.6120 odd ratios) and 1.3155 (resulting in 3.7265 odd ratios), respectively. The study concludes that if the poor attitude or perception towards complying with the preventive measures continues, COVID-19 cases in Africa may increase beyond the current spread.

Adjective Phrases in PNL and its Application to Reverse Dictionary

Owing to the large information content of adjectives in natural language-based data, we have dealt with the subject of adjective phrases in the paradigm of PNL. We have proposed a GCR form for the same and demonstrated its application in building a specific instance of Reverse Dictionary. A Reverse Dictionary takes a natural language description of a concept in the user’s mind as input and generates semantically appropriate word/s as output. For example, it could accept descriptions like ‘to run slowly,’ ‘to run but not very fast,’ etc., for the word ‘jog’. As Reverse Dictionary deals with natural language, handling perception-based information is important in its context for capturing the user intent. The D-RD, Reverse Dictionary for Descriptive words, designed in this paper, takes a natural language description of a human as input and generates appropriate adjectives for usage in text/speech as output. As a part of the design, we have proposed a novel CWW based similarity measure for calculating semantic similarity between adjective phrases. The D-RD is evaluated against two benchmarks, Onelook.com and WantWords Online RD, and is reported to outperform both for the test set under consideration.

Semantic transparency is not invisibility: A computational model of perceptually-grounded conceptual combination in word processing

Abstract Previous studies found that an automatic meaning-composition process affects the processing of morphologically complex words, and related this operation to conceptual combination. However, research on embodied cognition demonstrates that concepts are more than just lexical meanings, rather being also grounded in perceptual experience. Therefore, perception-based information should also be involved in mental operations on concepts, such as conceptual combination. Consequently, we should expect to find perceptual effects in the processing of morphologically complex words. In order to investigate this hypothesis, we present the first fully-implemented and data-driven model of perception-based (more specifically, vision-based) conceptual combination, and use the predictions of such a model to investigate processing times for compound words in four large-scale behavioral experiments employing three paradigms (naming, lexical decision, and timed sensibility judgments). We observe facilitatory effects of vision-based compositionality in all three paradigms, over and above a strong language-based (lexical and semantic) baseline, thus demonstrating for the first time perceptually grounded effects at the sub-lexical level. This suggests that perceptually-grounded information is not only utilized according to specific task demands but rather automatically activated when available.

Fuzzy Geometry-Based Decision Making with Unprecisiated Visual Information

Decision making is conditioned by relevant information. This information very seldom has reliable numerical representation. Usually, decision-relevant information is perception-based. A question arises of how to proceed from perception-based information to a corresponding mathematical formalism. When perception-based information is expressed in natural language, the fuzzy set theory can be used as a corresponding mathematical formalism for decision analysis. However, perception-based decision-relevant information is not always sufficiently clear to be modeled by means of membership functions. In contrast, it remains at a level of some cloud images which are difficult to be caught by words. This imperfect information caught in perceptions cannot be precisiated by numbers or fuzzy sets and is referred to as unprecisiated information. Humans are able to make decisions based on unprecisiated visual perceptions. Modeling of this outstanding capability, even to some limited extent, becomes a difficult yet a highly promising research area. In this study, we use fuzzy geometry and the extended fuzzy logic to cope with uncertain situations coming with unprecisiated information. In this approach, the objects of computation and reasoning are geometric primitives which model human perceptions when the latter cannot be defined in terms of membership functions. For this aim, the fuzzified axioms of the incidence geometry are used. An approach to decision making with outcomes and probabilities described by geometrical primitives is developed. Examples of application of the approach to decision making on a short term investment decision and marketing decision are given. The obtained results prove the validity of the suggested approach.

Cognitive Barriers to the Engagement of Wealthy Private Investors in Sustainable Investing

The notion that sustainable investing (SI) has become a mainstream investment style is challenged by the observation that private investors appear substantially underrepresented in SI compared to institutional investors. This study empirically explores SI-barriers for private investors. We provide insights from interviews with High Net Worth Individuals (HNWIs), an economically highly significant yet secretive and thus little researched segment of private investors. Our results indicate substantial, individually different interest in SI amongst HNWIs, and propose three specific SI-barriers. The first barrier pertains to a perception-based information asymmetry. Investment advisors appear to misinterpret clients’ individually specific interest in SI. Barrier two relates to HNWIs’ perception of SI as limited to themes that are above-average volatile. This perception limits the engagement in SI in case of a short investment time horizon. Barrier three likewise relates the perception of SI as volatile to the effect of financial losses. We reflect on the observed significance of investors’ perceived volatility of SI, investment time horizon and financial losses by relating them to Prospect Theory. In sum, investors’ cognition of SI appears to be an important engagement reason that goes beyond the usual business-case-arguments and that deserves more attention in future research.

Linkages within institutional structure: an empirical analysis of water institutions

AbstractTaking water institutions as an illustrative context, this paper attempts a quantitative analysis of the structural and functional linkages within institutional structure and indicates their performance implications and strategic importance for promoting institutional reforms. Against an overview of existing empirical works on institutions in general and water institutions in particular, the paper develops an analytical framework for specifying alternative models of institution–performance interaction within the water sector under different assumptions concerning institutional linkages and their structural properties. These models are, then, empirically estimated using the perception-based information provided by an international panel of 127 water experts from 43 countries and regions around the world. Based on the model results, the paper offers quantitative evidences for institutional linkages and their performance implications, and concludes by indicating their policy roles, especially in developing some reform design and implementation principles useful to overcome the technical and political economy constraints for institutional reforms.

Perception-based approach to time series data mining

Time series data mining (TSDM) techniques permit exploring large amounts of time series data in search of consistent patterns and/or interesting relationships between variables. TSDM is becoming increasingly important as a knowledge management tool where it is expected to reveal knowledge structures that can guide decision making in conditions of limited certainty. Human decision making in problems related with analysis of time series databases is usually based on perceptions like “end of the day”, “high temperature”, “quickly increasing”, “possible”, etc. Though many effective algorithms of TSDM have been developed, the integration of TSDM algorithms with human decision making procedures is still an open problem. In this paper, we consider architecture of perception-based decision making system in time series databases domains integrating perception-based TSDM, computing with words and perceptions, and expert knowledge. The new tasks which should be solved by the perception-based TSDM methods to enable their integration in such systems are discussed. These tasks include: precisiation of perceptions, shape pattern identification, and pattern retranslation. We show how different methods developed so far in TSDM for manipulation of perception-based information can be used for development of a fuzzy perception-based TSDM approach. This approach is grounded in computing with words and perceptions permitting to formalize human perception-based inference mechanisms. The discussion is illustrated by examples from economics, finance, meteorology, medicine, etc.

Dual-task interference is greater in delayed grasping than in visually guided grasping

Previous kinematic research suggests that visually guided grasping employs an accurate real-time control system in the dorsal stream, whereas delayed grasping relies on less accurate stored information derived by the perceptual system in the ventral stream. We explored these ideas in two experiments combining visually guided and delayed grasping with auditory tasks involving perception-based imagery and semantic memory. In both experiments, participants were cued to grasp three-dimensional objects of varying sizes. During visually guided trials, objects were visible during the interval between the cue and movement onset. During delayed trials, objects were occluded at the time of the cue. In Experiment 1, the second task required participants to listen to object names and vocally respond if the objects were of a particular shape. In Experiment 2, participants studied a paired-associates list prior to testing and then performed cued recall while grasping. The results of these experiments showed that there was reciprocal interference on both tasks, which was consistently greater during delayed grasping. Experiment 2 showed that the introduction of the second task resulted in larger grip apertures during delayed grasping. This supports the idea that delayed grasping involves processing of stored perception-based information that shares resources with cross-modal tasks involving imagery and memory.

Conditional probability and fuzzy information

The main subject of this paper is the embedding of fuzzy set theory—and related concepts—in a coherent conditional probability scenario. This allows to deal with perception-based information—in the sense of Zadeh—and with a rigorous treatment of the concept of likelihood, dealing also with its role in statistical inference. A coherent conditional probability is looked on as a general non-additive “uncertainty” measure m ( · ) = P ( E | · ) of the conditioning events. This gives rise to a clear, precise and rigorous mathematical frame, which allows to define fuzzy subsets and to introduce in a very natural way the counterparts of the basic continuous T-norms and the corresponding dual T-conorms, bound to the former by coherence. Also the ensuing connections of this approach to possibility theory and to information measures are recalled.

Random and fuzzy sets in coarse data analysis

The theoretical aspects of statistical inference with imprecise data, with focus on random sets, are considered. On the setting of coarse data analysis imprecision and randomness in observed data are exhibited, and the relationship between probability and other types of uncertainty, such as belief functions and possibility measures, is analyzed. Coarsening schemes are viewed as models for perception-based information gathering processes in which random fuzzy sets appear naturally. As an implication, fuzzy statistics is statistics with fuzzy data. That is, fuzzy sets are a new type of data and as such, complementary to statistical analysis in the sense that they enlarge the domain of applications of statistical science.

Fuzzy and random sets

The two distinct concepts of fuzzy and random sets coexist to form the basics for modeling perception-based information gathering processes in artificial intelligence. These processes, viewed as coarsening schemes, provide also justifications for various types of uncertainty measures such as belief functions and possibility measures.

Conditioning in a coherent setting: Theory and applications

In this paper we discuss the important concept of conditioning, emphasizing the role of a coherent setting for a general approach able to encompass many existing theories of uncertainty. Among conditional measures, we deepen here conditional probability, which allows to deal also, from a unifying point of view and with a new perspective, with concepts such as fuzzy sets theory and the relevant perception-based information, possibility measures, information measures, stochastic independence, default reasoning.

A note on web intelligence, world knowledge and fuzzy logic

Existing search engines––with Google at the top––have many remarkable capabilities; but what is not among them is deduction capability––the capability to synthesize an answer to a query from bodies of information which reside in various parts of the knowledge base. In recent years, impressive progress has been made in enhancing performance of search engines through the use of methods based on bivalent logic and bivalent-logic-based probability theory. But can such methods be used to add nontrivial deduction capability to search engines, that is, to upgrade search engines to question-answering systems? A view which is articulated in this note is that the answer is “No.” The problem is rooted in the nature of world knowledge, the kind of knowledge that humans acquire through experience and education. It is widely recognized that world knowledge plays an essential role in assessment of relevance, summarization, search and deduction. But a basic issue which is not addressed is that much of world knowledge is perception-based, e.g., “it is hard to find parking in Paris,” “most professors are not rich,” and “it is unlikely to rain in midsummer in San Francisco.” The problem is that (a) perception-based information is intrinsically fuzzy; and (b) bivalent logic is intrinsically unsuited to deal with fuzziness and partial truth. To come to grips with fuzziness of world knowledge, new tools are needed. The principal new tool––a tool which is briefly described in this note––is Precisiated Natural Language (PNL). PNL is based on fuzzy logic and has the capability to deal with partiality of certainty, partiality of possibility and partiality of truth. These are the capabilities that are needed to be able to draw on world knowledge for assessment of relevance, and for summarization, search and deduction.

Toward a perception-based theory of probabilistic reasoning with imprecise probabilities

The perception-based theory of probabilistic reasoning which is outlined in this paper is not in the traditional spirit. Its principal aim is to lay the groundwork for a radical enlargement of the role of natural languages in probability theory and its applications, especially in the realm of decision analysis. To this end, probability theory is generalized by adding to the theory the capability to operate on perception-based information, e.g., “Usually Robert returns from work at about 6 p.m.” or “It is very unlikely that there will be a significant increase in the price of oil in the near future”. A key idea on which perception-based theory is based is that the meaning of a proposition, p, which describes a perception, may be expressed as a generalized constraint of the form X is r R, where X is the constrained variable, R is the constraining relation and is r is a copula in which r is a discrete variable whose value defines the way in which R constrains X. In the theory, generalized constraints serve to define imprecise probabilities, utilities and other constructs, and generalized constraint propagation is employed as a mechanism for reasoning with imprecise probabilities as well as for computation with perception-based information.

Incorporation of perception-based information in robot learning using fuzzy reinforcement learning agents

Robot learning in unstructured environments has been proved to be an extremely challenging problem, mainly because of many uncertainties always present in the real world. Human beings, on the other hand, seem to cope very well with uncertain and unpredictable environments, often relying on perception-based information. Furthermore, humans beings can also utilize perceptions to guide their learning on those parts of the perception-action space that are actually relevant to the task. Therefore, we conduct a research aimed at improving robot learning through the incorporation of both perception-based and measurement-based information. For this reason, a fuzzy reinforcement learning (FRL) agent is proposed in this paper. Based on a neural-fuzzy architecture, different kinds of information can be incorporated into the FRL agent to initialise its action network, critic network and evaluation feedback module so as to accelerate its learning. By making use of the global optimisation capability of GAs (genetic algorithms), a GA-based FRL (GAFRL) agent is presented to solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can perform a more effective global search. Different GAFRL agents are constructed and verified by using the simulation model of a physical biped robot. The simulation analysis shows that the biped learning rate for dynamic balance can be improved by incorporating perception-based information on biped balancing and walking evaluation. The biped robot can find its application in ocean exploration, detection or sea rescue activity, as well as military maritime activity.

Fuzzy control rules extraction from perception-based information using computing with words

In this paper, a novel approach to extract fuzzy control rules from perception-based information using computing with (CW) is firstly presented. The approach is based on applying fuzzy numbers and their arithmetic operations. The fuzzy Lyapunov synthesis, which gives a linguistic description on the plant and the control objective, is used to design a stable fuzzy controller. Secondly, that the traditional fuzzy-control rules can be derived systematically, rather than heuristically, and their stability can be guaranteed is demonstrated. Moreover, by introducing fuzzy numbers and their arithmetic operations, the words represented by fuzzy numbers could be efficiently manipulated for fuzzy controller design. The performance and applicability of the proposed method are illustrated through the practical implementation of fuzzy control of a pole-balancing vehicle.

A New Direction in AI: Toward a Computational Theory of Perceptions

Fast-forward (FF) was the most successful automatic planner in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS '00) planning systems competition. Like the well-known hsp system, FF relies on forward search in the state space, guided by a heuristic that estimates goal distances by ignoring delete lists. It differs from HSP in a number of important details. This article describes the algorithmic techniques used in FF in comparison to hsp and evaluates their benefits in terms of run-time and solution-length behavior. Humans have a remarkable capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Familiar examples are parking a car, driving in city traffic, playing golf, cooking a meal, and summarizing a story. In performing such tasks, humans use perceptions of time, direction, speed, shape, possibility, likelihood, truth, and other attributes of physical and mental objects. Reflecting the bounded ability of the human brain to resolve detail, perceptions are intrinsically imprecise. In more concrete terms, perceptions are f-granular, meaning that (1) the boundaries of perceived classes are unsharp and (2) the values of attributes are granulated, with a granule being a clump of values (points, objects) drawn together by indistinguishability, similarity, proximity, and function. For example, the granules of age might be labeled very young, young, middle aged, old, very old, and so on. F-granularity of perceptions puts them well beyond the reach of traditional methods of analysis based on predicate logic or probability theory. The computational theory of perceptions (CTP), which is outlined in this article, adds to the armamentarium of AI a capability to compute and reason with perception-based information. The point of departure in CTP is the assumption that perceptions are described by propositions drawn from a natural language; for example, it is unlikely that there will be a significant increase in the price of oil in the near future. In CTP, a proposition, p, is viewed as an answer to a question, and the meaning of p is represented as a generalized constraint. To compute with perceptions, their descriptors are translated into what is called the generalized constraint language (GCL). Then, goal-directed constraint propagation is utilized to answer a given query. A concept that plays a key role in CTP is that of precisiated natural language (PNL). The computational theory of perceptions suggests a new direction in AI -- a direction that might enhance the ability of AI to deal with realworld problems in which decision-relevant information is a mixture of measurements and perceptions. What is not widely recognized is that many important problems in AI fall into this category.