Trapped within a ‘wall of sound’ A possible mechanism for the bubble nets of humpback whales

Marek Sergot’s technical contributions range over different subjects. He has developed a series of novel ideas and formal methods bridging different research domains, such as artificial intelligence, computational logic, philosophical logic, legal theory, artificial intelligence and law, multi

that the agents ’ interests are best served by behaving correctly. Following notions from the field of mechanism design, we suggest a framework for studying such algorithms. Our main technical contribution concerns the study of a representative task scheduling problem for which the standard mechanism design tools

sequential data. In particular, the main novel technical contributions of this thesis are as follows: a way of representing Hierarchical HMMs as DBNs, which enables inference to be done in O(T) time instead of O(T 3), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T

activation at near-optimal levels through planned stimulus change, then the theory can contribute substantially to increasing the usefulness of job rotation as a motivational technique. If, however, it turns out that there are general and inevitable decreases in activation over time regardless of how

Abstract Learning in automated negotiation is a difficult problem because the target function is hidden and the available experience for learning is rather limited. Transfer learning is a branch of machine learning research con-cerned with the reuse of previously acquired knowledge in new learning tasks, for example, in order to reduce the amount of learning experience required to attain a certain level of performance. This paper proposes a novel strategy based on a variation of TrAdaBoost—a classic instance transfer technique—that can be used in a multi-issue negotiation setting. The experimental results show that the proposed method is effective in a variety of application domains against the state-of-the-art negotiating agents.

for papers by our external faculty, papers must be based on work done at SFI, inspired by an invited visit to or collaboration at SFI, or funded by an SFI grant. ©NOTICE: This working paper is included by permission of the contributing author(s) as a means to ensure timely distribution of the scholarly

Abstract We introduce and investigate formal quantita-tive measures of inconsistency between the beliefs of agents in multi-agent systems. We start by recalling a well-known model of belief in multi-agent systems, and then, using this model, present two classes of inconsistency metrics. First, we consider metrics that attempt to charac-terise the overall degree of inconsistency of a multi-agent system in a single numeric value, where inconsistency is considered to be individuals within the system having contradictory beliefs. While this metric is useful as a high-level indicator of the degree of inconsistency between the beliefs of members of a multi-agent system, it is of limited value for understanding the structure of inconsistency in a system: it gives no indication of the sources of inconsis-tency. We therefore introduce metrics that quantify for a given individual the extent to which that individual is in conflict with other members of the society. These metrics are based on power indices, which were developed within the cooperative game theory community in order to understand the power that individuals wield in cooperative settings.

Abstract Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols denoting physical objects and sensor data being collected about them, a process called anchoring. In this paper we present a stream-based hierarchical anchoring framework. A classification hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of classification, permitting symbolic reasoning on different levels of abstraction. The approach is integrated in the DyKnow knowledge processing middleware and has been

This paper discusses ZBMs technical contributions to high level programming languages from the viewpoint of speciJic languages and their contributions to the technology. The philosophy used in this paper is that it is the appropriate collection of features in a language which generally makes