Many approaches have been introduced for representing and solving multiagent coordination problems. Unfortunately, these methods make assumptions that limit their usefulness when combined with human operators and real-life hardware and software. In this paper, we discuss the problem of using agents in conjunction with human operators to improve coordination as well as possible models that could be used in these problems. Our approach — Space Collaboration via an Agent Network (SCAN) — enables proxy agents to represent each of the stakeholder agencies in a space setting and shows how the SCAN agent network could facilitate collaboration by identifying opportunities and methods of collaboration. We discuss this approach as well as the challenges in extending models to 1) take advantage of human input, 2) deal with the limited and uncertain information that will be present and 3) combat the scalability issues in solution methods for a large number of decentralized agents. As a first step toward providing rich models for these domains, we describe a method to bound the solution quality due to bounded model uncertainty.

The paper develops a formal model for interpreting vague languages based on a variant of "supervaluation" semantics. Two modes of semantic variability are modelled, corresponding to different aspects of vagueness: one mode arises where there can be multiple definitions of a term; and the other relates to the threshold of applicability of a vague term with respect to the magnitude of relevant observable values. The truth of a proposition depends on both the possible world and the "precisification" with respect to which it is evaluated. Structures representing possible worlds and precisifications are both specified in terms of primitive functions representing observable measurements, so that the semantics is grounded upon an underlying theory of physical reality. On the basis of this semantics, the acceptability of a proposition to an agent is characterised in terms of a combination of agent's beliefs about the world and their attitude to admissible interpretations of vague predicates.

This paper investigates the use of high-level action languages for representing and reasoning about commitments in mulit-agent domains. The paper introduces the language L mt with features motivates by the problem of representing commitments; in particular, it shows how L mt can handle both simple commitment actions and complex commitment protocols. The semantics of L mt provides a uniform solution to different problems in reasoning about commitments, e.g., the problem of (i) verifying whether an agent fails (or succeeds) to deliver on its commitments; (ii) identifying pending commitments; and (iii) suggesting ways to satisfy pending commitments.

Inspired by the Rapidly Exploring Random Tree data-structure and algorithm for path planning in high-dimensional, continuous spaces, we consider an approach for spanning a space with a group of simple robots. We employ a minimalist approach in which InfraRed and contact sensors form the primary means of communication; the agents physically embody the elements of the tree through their position and other agents can either follow the tree to useful locations or expand the tree by becoming part of it. Although robots are constrained in some of the operations they may perform in space, we argue that our approach remains consistent with the original data-structure. We demonstrate that one may perform a planning query from a point to the tree origin directly via message passing where passing involves direct physical motion or simple IR messages. Based on the work done by Werger and Matarić , our implementation proves that it is possible to form and maintain a RRT using simple position unaware robots. The work is important because it demonstrates that decentralized path planning can be performed by simple agents using purely reactive behaviors and at the same time poses significant challenges to keep the shape of the tree intact.

Cooperation in multiagent systems essentially hinges on appropriate communication. This paper shows how to model communication in teamwork within TeamLog, the first multi-modal framework wholly capturing a methodology for working together. Taking off from the dialogue theory of Walton and Krabbe, the paper focuses on deliberation, the main type of dialogue during team planning. We provide a four-stage schema of deliberation dialogue along with semantics of adequate speech acts, filling the gap in logical modeling of communication during planning.

The challenge of using communications infrastructure to stabilize other infrastructures is related to research on the collective communications systems in social animals, robots, and human-non-human interaction. In these systems, voting models can explicate patterns of observed behavior or predict collective outcomes. Developing more theoretical deductive explanatory power can increase our knowledge about the interplay of voters and communication that produces collective inferences. This paper suggests that many analyses of voting patterns have not integrated what is known about the predictive properties of voting processes into their analyses. Taking a more deductive approach enables us to think about the strengths and weaknesses of existing explanations and imagine new types of analysis that have implications for engineering communications systems to stabilize other infrastructures.

Stackelberg games have been widely used to model patrolling or monitoring problems in security. In a Stackelberg security game, the defender commits to a strategy and the adversary makes its decision with knowledge of the leader's commitment. Algorithms for computing the defender's optimal strategy are used in deployed decision-support tools in use by the Los Angeles International Airport (LAX), the Federal Air Marshals Service, and the Transportation Security Administration (TSA). Those algorithms take into account various resource usage constraints defined by human users. However, those constraints may lead to poor (even infeasible) solutions due to users' insufficient information and bounded rationality. A mixed-initiative approach, in which human users and software assistants (agents) collaborate to make security decisions, is needed. Efficient human-agent interaction process leads to models with higher overall solution quality. This paper preliminarily analyzes the needs and challenges for such a mixed-initiative approach.

We formalize actions and change in a housekeeping domain with multiple cleaning robots, and commonsense knowledge about this domain, in the action language C+. Geometric reasoning is lifted to high-level representation by embedding motion planning in the domain description using external predicates. With such a formalization of the domain, a plan can be computed using the causal reasoner CCalc for each robot to tidy some part of the house. We introduce a planning and monitoring algorithm for safe execution of these plans, so that it can recover from plan failures due to collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted alone. We illustrate the applicability of this algorithm with a simulation of a housekeeping domain.

Within the context of human/multi-robot teams, the "help me help you" paradigm offers different opportunities. A team of robots can help a human operator accomplish a goal, and a human operator can help a team of robots accomplish the same, or a different, goal. Two scenarios are examined here. First, a team of robots helps a human operator search a remote facility by recognizing objects of interest. Second, the human operator helps the robots improve their position (localization) information by providing quality control feedback.

An emotional version of Sapir-Whorf hypothesis suggests that differences in language emotionalities influence differences among cultures no less than conceptual differences. Conceptual contents of languages and cultures to significant extent are determined by words and their semantic differences; these could be borrowed among languages and exchanged among cultures. Emotional differences, as suggested in the paper, are related to grammar and mostly cannot be borrowed. Conceptual and emotional mechanisms of languages are considered here along with their functions in the mind and cultural evolution. A fundamental contradiction in human mind is considered: language evolution requires reduced emotionality, but "too low" emotionality makes language "irrelevant to life," disconnected from sensory-motor experience. Neural mechanisms of these processes are suggested as well as their mathematical models: the knowledge instinct, the language instinct, the dual model connecting language and cognition, dynamic logic, neural modeling fields. Mathematical results are related to cognitive science, linguistics, and psychology. Experimental evidence and theoretical arguments are discussed. Approximate equations for evolution of human minds and cultures are obtained. Their solutions identify three types of cultures: "conceptual"-pragmatic cultures, in which emotionality of language is reduced and differentiation overtakes synthesis resulting in fast evolution at the price of uncertainty of values, self doubts, and internal crises; "traditional-emotional" cultures where differentiation lags behind synthesis, resulting in cultural stability at the price of stagnation; and "multi-cultural" societies combining fast cultural evolution and stability. Unsolved problems and future theoretical and experimental directions are discussed.