Meta-level Control in Multi-Agent Systems.

Abstract Sophisticated agents operating in open environments must make decisions that efficiently trade off the use of their limited resources between dynamic deliberative actions and domain actions? This is the meta-level control problem for agents operating in resource-bounded multi-agent environments. Control activities involve decisions on when to invoke and the amount to effort to put into scheduling and coordination of domain activities. The focus of this paper is how to make effective meta-level control decisions. We show that meta-level control with bounded computational overhead allows complex agents to solve problems more efficiently than current approaches in dynamic open multi-agent environments. The meta-level control approach that we present is based on the decision-theoretic use of an abstract representation of the agent state. This abstraction concisely captures critical information necessary for decision making while bounding the cost of meta-level control and is appropriate for use in automatically learning the meta-level control policies

Building Ethics into Artificial Intelligence

As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies

Evolving the best-response strategy to decide when to make a proposal

Abstract-This paper designed and developed negotiation agents with the distinguishing features of 1) conducting continuous time negotiation rather than discrete time negotiation, 2) learning the response times of trading parties using Bayesian learning and, 3) deciding when to make a proposal using a multi-objective genetic algorithm (MOGA) to evolve their best-response proposing time strategies for different negotiation environments and constraints. Results from a series of experiments suggest that 1) learning trading parties' response times helps agents achieve more favorable trading results, and 2) on average, when compared with SSAs (Static Strategy Agents), BRSAs (Best-Response proposing time Strategy Agents) achieved higher average utilities, higher success rates in reaching deals, and smaller average negotiation time

Using organization knowledge to improve routing performance in wireless multi-agent networks

ABSTRACT Multi-agent systems benefit greatly from an organization design that guides agents in determining when to communicate, how often, with whom, with what priority, and so on. However, this same organization knowledge is not utilized by general-purpose wireless network routing algorithms normally used to support agent communication. We show that incorporating organization knowledge (otherwise available only to the application layer) in the network-layer routing algorithm increases bandwidth available at the application layer by as much as 35 percent. This increased bandwidth is especially important in communication-intensive application settings, such as agent-based sensor networks, where node failures and link dynamics make providing sufficient inter-agent communication especially challenging