In the literature on reasoning on the basis of evidence, two traditions exist: one argument-based, and one based on narratives. Recently, we have proposed a hybrid perspective in which argumentation and narratives are combined. This formalized hybrid theory has been tested in a sense-making software prototype for criminal investigators and decision makers. In the present paper, we elaborate on the role of commonsense knowledge. We argue that two kinds of knowledge are essential: argumentation schemes and story schemes. We discuss some of the research issues that need to be addressed.

In this paper, we explore ideas regarding a formal logical model which allows for the use of stories to persuade autonomous software agents to take a particular course of action. This model will show how typical stories – sequences of events that form a meaningful whole – can be used to set an example for an agent and how the agent might adapt his own values and choices according to the values and choices made by the characters in the story.

Using the well-known Artificial Anasazi simulation for a case study, we investigate the use of genetic algorithms (GAs) for performing two common tasks related to robustness checking of agent-based models: parameter calibration and sensitivity analysis. In the calibration task, we demonstrate that a GA approach is able to find parameters that are equally good or better at minimizing error versus historical data, compared to a previous factorial grid-based approach. The GA approach also allows us to explore a wider range of parameters and parameter settings. Previous univariate sensitivity analysis on the Artificial Anasazi model did not consider potentially complex/nonlinear interactions between parameters. With the GA-based approach, we perform multivariate sensitivity analysis to discover how greatly the model can diverge from historical data, while the parameters are constrained within a close range of previously calibrated values. We show that by varying multiple parameters within a 10% range, the model can produce dramatically and qualitatively different results, and further demonstrate the utility of sensitivity analysis for model testing, by the discovery of a small coding error. Through this case study, we discuss some of the issues that can arise with calibration and sensitivity analysis of agent-based models.

Theory suggests that biological robustness allows for the maintenance of fitness in the face of mutational change, and to the extent that this mutational change translates to heritable phenotypic change, that biological robustness allows for evolvability. However, empirical demonstrations that robustness promotes evolvability remain scant. This is in part due to the difficulty of defining and measuring both evolvability and robustness in real biological systems. Here we test whether protein structural robustness is associated with the extent of adaptive change a protein experiences. We find this to be the case for two forms of protein robustness—designability and modularity, which we measure via contact density and helix/sheet density, respectively. We interpret this association to be primarily the result of reduced constraints on amino acid substitutions in highly designable and/or modular proteins, resulting in less antagonistic pleiotropy and faster adaptation through natural selection.

MAMmodels are inherent in CAS as a holistic System. Multi-agent modeling is based on "down-up" Many surprising properties of the Economic Systems (such methodology, starting from the interaction of a multitude as sudden crises, jumps of macro-indices, catastrophe-like of "agents" to revealing the emergent properties of the changes of the system) can be understood deeper on the integral system.

Here we present a network model in which self-sustaining recurrent activation emerges from simple cascades of activation. It is demonstrated that the ability to support such self-sustaining activation in our model depends on network connectivity as well as the ability to grow new links over time. Additionally, we explore how the probability of emergence of self-sustaining activity can be modulated by changing various network parameters and suggest potential applications of our findings.

In this paper we investigate the performance of Cultural Algorithms over the complete range of system complexities, from fixed to chaotic.In order to apply the Cultural Algorithm over all complexity classes we generalize on its co-evolutionary nature to keep the variation in the population across all complexities. Based on previous cultural algorithm approaches, we were to extend the existing models to produce a more general one that could be applied across all complexity classes. We produced a new version of the Cultural Algorithms Toolkit, CAT 2.0, which supported a variety of co-evolutionary features at both the Knowledge and Population levels. We then applied the system to the solution of a 150 randomly generated problems that ranged from simple to chaotic complexity classes. As a result we were able to produce the following conclusions: No homogeneous Social Fabric tested was dominant over all categories of complexity. As the complexity of problems increased, so did the complexity of the Social Fabric that was need to deal with it efficiently. In other words, there was experimental evidence that social structure can be related to the frequency and complexity type of the problems that presented to a cultural system.

A large body of research describes the importance of adaptability for systems to be resilient in the face of disruptions. However, adaptive processes can be fallible, either because systems fail to adapt in situations requiring new ways of functioning, or because the adaptations themselves produce undesired consequences. A central question is then: how can systems better manage their capacity to adapt to perturbations, and constitute intelligent adaptive systems? Based on studies conducted in different high-risk domains (healthcare, mission control, military operations, urban firefighting), we have identified three basic patterns of adaptive failures or traps: (1) decompensation – when a system exhausts its capacity to adapt as disturbances and challenges cascade; (2) working at cross-purposes – when sub-systems or roles exhibit behaviors that are locally adaptive but globally maladaptive; (3) getting stuck in outdated behaviors – when a system over-relies on past successes although conditions of operation change. The identification of such basic patterns then suggests ways in which a work organization, as an example of a complex adaptive system, needs to behave in order to see and avoid or recognize and escape the corresponding failures. The paper will present how expert practitioners exhibit such resilient behaviors in high-risk situations, and how adverse events can occur when systems fail to do so. We will also explore how various efforts in research related to complex adaptive systems provide fruitful directions to advance both the necessary theoretical work and the development of concrete solutions for improving systems’ resilience.

We propose a collaborative approach to the issue of resource creation for commonsense computing by developing a collaboratory application aimed at children. Human validation is enabled through a game-with-a-purpose (GWAP) interface, gathering reliability judgements of assertions that can be used to aid the process of resource validation. Our experiments confirm that children aged 10 to 12 can be valuable and reliable partners in building commonsense databases, due to their stage of mental development and their eagerness to play GWAPs. Results show that children adapt their word choice in the assertions they provide to the difficulty level of the stimuli words, and that the judgements gathered through in-game validation can help to validate about 30% of the gathered statements automatically.

This paper presents our work in developing a commonsense knowledge source based on semantic concepts about objects, activities and their relationships in a child’s daily life. This commonsense ontology is then used by our automatic story generator to output children's stories of the fable form from a given input picture. The generated story is a narration of the events of a basic plot that flows from negative to positive (rule violation to value acquisition), using themes that are familiar to children. The paper ends with descriptions of further investigations that are underway to extend the system, including using a formal upper ontology to represent storytelling knowledge, and the generation of stories from a given set of sequential scenes.