Mapping knowledge management and organizational learning in support of organizational memory

The normative literature within the field of Knowledge Management has concentrated on techniques and methodologies for allowing knowledge to be codified and made available to individuals and groups within organizations. The literature on Organizational Learning however, has tended to focus on aspects of knowledge that are pertinent at the macro-organizational level (i.e. the overall business). The authors attempt in this paper to address a relative void in the literature, aiming to demonstrate the inter-locking factors within an enterprise information system that relate knowledge management and organizational learning, via a model that highlights key factors within such an inter-relationship. This is achieved by extrapolating data from a manufacturing organization using a case study, with these data then modeled using a cognitive mapping technique (Fuzzy Cognitive Mapping, FCM). The empirical enquiry explores an interpretivist view of knowledge, within an Information Systems Evaluation (ISE) process, through the associated classification of structural, interpretive and evaluative knowledge. This is achieved by visualizng inter-relationships within the ISE decision-making approach in the case organization. A number of decision paths within the cognitive map are then identified such that a greater understanding of ISE can be sought. The authors therefore present a model that defines a relationship between Knowledge Management (KM) and Organisational Learning (OL), and highlights factors that can lead a firm to develop itself towards a learning organization

Design Principals of Social Navigation

8th Delos Workshop on "User Interfaces for Digital Libraries" (on 21 October it will be held in conjuction with the 4th ERCIM Workshop on "User Interfaces for All"), SICS, Kista, Sweden, 21-23 October 1998PERSON

SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequences to Navigate Towards a Rewarded Goal

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.Riverside Reserach Institute; Defense Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225); National Science Foundation (IRI 90-24877, SBE-0345378); Office of Naval Research (N00014-92-J-1309, N00014-91-J-4100, N00014-01-1-0624, N00014-01-1-0624); Pacific Sierra Research (PSR 91-6075-2

Analysis of Crimonogenic Factors in Femicide Crimes

Femicide in Ecuador is one of the cruelest manifestations of power exercised over women. Actions have been executed, but they are still insufficient. Therefore, it is necessary to study the factors that cause its high level of incidence. By doing so, we intend to provide the government with a tool that makes it possible to focus some measures in order to reduce and/or eradicate it. The objective of this research is to analyze these factors through neutrosophic cognitive maps. This technique is chosen because of the advantages it offers compared to other soft-computing techniques, in terms of interpretability, scalability, aggregation of knowledge, dynamism and its ability to represent feed-back and indeterminacy relationships. This way, decision-making by the government will be facilitated by hierarchizing the incidence factors, launching new strategies to eradicate femicide and to protect the right to life

On the interpretability of fuzzy cognitive maps

This paper proposes a post-hoc explanation method for computing concept attribution in Fuzzy Cognitive Map (FCM) models used for scenario analysis, based on SHapley Additive exPlanations (SHAP) values. The proposal is inspired by the lack of approaches to exploit the often-claimed intrinsic interpretability of FCM models while considering their dynamic properties. Our method uses the initial activation values of concepts as input features, while the outputs are considered as the hidden states produced by the FCM model during the recurrent reasoning process. Hence, the relevance of neural concepts is computed taking into account the model’s dynamic properties and hidden states, which result from the interaction among the initial conditions, the weight matrix, the activation function, and the selected reasoning rule. The proposed post-hoc method can handle situations where the FCM model might not converge or converge to a unique fixed-point attractor where the final activation values of neural concepts are invariant. The effectiveness of the proposed approach is demonstrated through experiments conducted on real-world case studies