Product take-back legislation forces manufacturers to bear the costs of collection and disposal of products that have reached the end of their useful lives. In order to reduce these costs, manufacturers can consider reuse, remanufacturing and/or recycling of components as an alternative to disposal. The implementation of such alternatives usually requires an appropriate reverse supply chain management. With the concepts of reverse supply chain are gaining popularity in practice, the use of artificial intelligence approaches in these areas is also becoming popular. As a result, the purpose of this paper is to give an overview of the recent publications concerning the application of artificial intelligence techniques to reverse supply chain with emphasis on certain types of product returns.

Graph coloring, also known as vertex coloring, considers the problem of assigning colors to the nodes of a graph such that adjacent nodes do not share the same color. The optimization version of the problem concerns the minimization of the number of used colors. In this paper we deal with the problem of finding valid colorings of graphs in a distributed way, that is, by means of an algorithm that only uses local information for deciding the color of the nodes. Such algorithms prescind from any central control. Due to the fact that quite a few practical applications require to find colorings in a distributed way, the interest in distributed algorithms for graph coloring has been growing during the last decade. As an example consider wireless ad-hoc and sensor networks, where tasks such as the assignment of frequencies or the assignment of TDMA slots are strongly related to graph coloring. The algorithm proposed in this paper is inspired by the calling behavior of Japanese tree frogs. Male frogs use their calls to attract females. Interestingly, groups of males that are located nearby each other desynchronize their calls. This is because female frogs are only able to correctly localize the male frogs when their calls are not too close in time. We experimentally show that our algorithm is very competitive with the current state of the art, using different sets of problem instances and comparing to one of the most competitive algorithms from the literature.

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.

Forming ad-hoc coalitions between military forces and humanitarian organizations is crucial in mission-critical scenarios. Very often coalition parties need to operate according to planning constraints and regulations, or policies. Therefore, they find themselves not only in need to consider their own goals, but also to support coalition partners to the extent allowed by such regulations. In time-stressed conditions, this is a challenging and cognition-intensive task. In this paper, we present intelligent agents that support human planners and ease their cognitive burden by detecting and giving advice about the violation of policies and constraints. Through a series of experiments conducted with human subjects, we compare and contrast the agents' performance on a number of metrics in three conditions: agent support, transparent policy enforcement, and neither support nor enforcement.

Among the many aspects of human intelligence that currently elude the simulation by machines is that of story understanding. Although many theories of narrative have been proposed, several processes pertaining to narrative remain inadequately formalized and, hence, beyond full mechanization. This work proposes a general formal framework that attempts to make precise such processes and related notions, with first and foremost that of what constitutes a narrative. Emphasis is placed on identifying certain premises that narratives are expected to adhere to, and deriving the formal implications that these have in terms of the computability of the various relevant notions. Among others, it is established that checking whether a discourse is a narrative is decidable, and that narratives can be computably enumerated and, hence, unambiguously indexed.

An experiment was conducted to test the efficacy of a new intelligent hypermedia system, MetaTutor, which is intended to prompt and scaffold the use of self-regulated learning (SRL) processes during learning about a human body system. Sixty-eight (N=68) undergraduate students learned about the human circulatory system under one of three conditions: prompt and feedback (PF), prompt-only (PO), and control (C) condition. The PF condition received timely prompts from animated pedagogical agents to engage in planning processes, monitoring processes, and learning strategies and also received immediate directive feedback from the agents concerning the deployment of the processes. The PO condition received the same timely prompts, but did not receive any feedback following the deployment of the processes. Finally, the control condition learned without any assistance from the agents during the learning session. All participants had two hours to learn using a 41-page hypermedia environment which included texts describing and static diagrams depicting various topics concerning the human circulatory system. Results indicate that the PF condition had significantly higher learning efficiency scores, when compared to the control condition. There were no significant differences between the PF and PO conditions. These results are discussed in the context of development of a fully-adaptive hypermedia learning system intended to scaffold self-regulated learning.

We summarize our past, present and future research related to human-robot dialogue, starting with its foundations in collaborative discourse theory, continuing to our current research on recognizing and generating engagement, and concluding with an outline of new work we are beginning on the modeling of long-term relationships between humans and robots.

The classic Lotka-Volterra equations present a mathematically robust and well-validated set of idealized equations for describing the predator-prey relationship found in many domains. Here we present results of formulating these equations using a Complex Adaptive Systems model, simulated using Agent-based Modeling techniques. This method allows for (a) closer study of the complex dynamics that are found in these systems, (b) greater understanding of the agent interactions, and (c) more realistic simulation outputs. In so doing, we have uncovered a novel relationship between the amount of resources found at the lowest tropic level of a hypothesized ecosystem and the highest tropic level predators. We explore these results in detail, and highlight their applicability to a real-world marine ecosystem.

We use the methods of evolutionary game theory and computational modelling to examine the evolution of ethnocentrism. We show that ethnocentrism evolves in a spatially structured population not only under prisoner's dilemma interactions, but also hawk-dove, assurance, harmony, and leader games. In the case of harmony, ethnocentrism evolves even when defection is irrational. This suggests that the pressure of competing for a common resource (in our model: free space) can produce irrational hostility between groups. The minimal cognitive assumptions in our model also suggest that the ethnocentrism observed in humans and elsewhere in nature has an evolutionary basis that is robust over changes in interaction types.

Successful learning with advanced learning technologies is based on the premise that learners adaptively regulate their cognitive and metacognitive behaviors during learning. However, there is abundant empirical evidence that suggests that learners typically do not adaptively modify their behavior, thus suggesting that they engage in what is called dysregulated behavior. Dysregulated learning is a new term that is used to describe a class of behaviors that learners use that lead to minimal learning. Examples of dysregulated learning include failures to: (1) encode contextual demands, (2) deploy effective learning strategies, (3) modify and update internal standards, (4) deal with the dynamic nature of the task, (5) metacognitive monitor the use of strategies and repeatedly make accurate metacognitive judgments, and (6) intelligently adapt behavior during learning so as to maximize learning and understanding of the instructional material. Understanding behaviors associated with dysregulated learning is critical since it has implications for determining what they are, when they occur, how often they occur, and how they can be corrected during learning.