Contextual and Possibilistic Reasoning for Coalition Formation

In multiagent systems, agents often have to rely on other agents to reach their goals, for example when they lack a needed resource or do not have the capability to perform a required action. Agents therefore need to cooperate. Then, some of the questions raised are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents' abilities to carry out certain tasks? In this article, we address the question of how to find and evaluate coalitions among agents in multiagent systems using MCS tools, while taking into consideration the uncertainty around the agents' actions. Our methodology is the following: We first compute the solution space for the formation of coalitions using a contextual reasoning approach. Second, we model agents as contexts in Multi-Context Systems (MCS), and dependence relations among agents seeking to achieve their goals, as bridge rules. Third, we systematically compute all potential coalitions using algorithms for MCS equilibria, and given a set of functional and non-functional requirements, we propose ways to select the best solutions. Finally, in order to handle the uncertainty in the agents' actions, we extend our approach with features of possibilistic reasoning. We illustrate our approach with an example from robotics

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

The Rise of Individual Performance Pay

Why does individual performance pay seem to prevail in human capital intensive industries? We present a model that may explain this. In a repeated game model of relational contracting, we analyze the conditions for implementing peer dependent incentive regimes when agents possess indispensable human capital. We show that the larger the share of values that the agents can hold-up, the lower is the implementable degree of peer dependent incentives. In a setting with team effects — complementary tasks and peer pressure, respectively — we show that while team-based incentives are optimal if agents are dispensable, it may be costly, and in fact suboptimal, to provide team incentives once the agents become indispensable.relational contracts, multiagent moral hazard, indispensable human capital

Exploring the resource recovery potentials of municipal solid waste: a review of solid wastes composting in developing countries

Population explosion, high urbanization and improved living standards have induced rapid changes in quantities and materiacompositions of solid waste generation globally. Until recently solid waste disposal in landfills and open dump sites waconsidered more economical and it is the most widely used methods in developing countries. Hence the potentials in the othealternative methods such as the resource recovery and recycling and their integration into waste management have been scarcelassessed. However, the ever growing challenges posed by the rapidly increasing quantities and compositions of solid wastes ideveloping countries led to the searching for alternative waste disposal methods. In this regard the paper presented an assessmenof the resource potentials of municipal solid waste materials arising from cities in developing countries as a strategy fosustainable solid waste management. Using published data on solid waste composition the paper has identified that there is higpotentials of composting in the solid waste stream from cities in developing countries. In conclusion, it recommended the recoverof organic waste material and papers for composting and the recycling of plastic, metals, textiles and others to explore their resource recovery potentials. This will largely reduce the ultimate quantities of solid waste for disposal and lower the operatincosts. This strategy will achieve sustainable waste management in developing countries. It is hoped that the paper has provided useful guide for wastes management policy decisions in developing countries

Integrating social power into the decision-making of cognitive agents

AbstractSocial power is a pervasive feature with acknowledged impact in a multitude of social processes. However, despite its importance, common approaches to social power interactions in multi-agent systems are rather simplistic and lack a full comprehensive view of the processes involved. In this work, we integrated a comprehensive model of social power dynamics into a cognitive agent architecture based on an operationalization of different bases of social power inspired by theoretical background research in social psychology. The model was implemented in an agent framework that was subsequently used to generate the behavior of virtual characters in an interactive virtual environment. We performed a user study to assess users' perceptions of the agents and found evidence supporting both the social power capabilities provided by the model and their value for the creation of believable and interesting scenarios. We expect that these advances and the collected evidence can be used to support the development of agent systems with an enriched capacity for social agent simulation