Estimating Information Value in Collaborative Multi-Agent Planning Systems

This paper addresses the problem of identifying the value of information held by a teammate on a distributed, multi-agent team. It focuses on a distributed scheduling task in which computer agents support people who are carrying out complex tasks in a dynamic environment. The paper presents a decision-theoretic algorithm for determining the value of information that is potentially relevant to schedule revisions, but is directly available only to the person and not the computer agent. The design of a "coordination autonomy" (CA) module within a coordination-manager system provided the empirical setting for this work. By design, the CA module depends on an external scheduler module to determine the specific effect of additional information on overall system performance. The paper describes two methods for reducing the number of queries the CA issues to the scheduler, enabling it to satisfy computational resource constraints placed on it. Experimental results indicate the algorithm improves system performance and establish the exceptional efficiency---measured in terms of the number of queries required for estimating the value of information---that can be achieved by the query-reducing methods.Engineering and Applied Science

Timing Interruptions for Better Human-Computer Coordinated Planning

The high operations tempo and growing complexity of planning (and re-planning) in various mission-critical domains suggest an approach in which systems act as primary planners rather than assisting the user in planning. We present a high-level overview of our design of a Coordination Autonomy (CA) module as part of such planning system, responsible to intelligently initiate and manage the necessary interactions with the user for enhancing the system's performance.Engineering and Applied Science

Sharing Experiences to Learn User Characteristics in Dynamic Environments with Sparse Data

This paper investigates the problem of estimating the value of probabilistic parameters needed for decision making in environments in which an agent, operating within a multi-agent system, has no a priori information about the structure of the distribution of parameter values. The agent must be able to produce estimations even when it may have made only a small number of direct observations, and thus it must be able to operate with sparse data. The paper describes a mechanism that enables the agent to significantly improve its estimation by augmenting its direct observations with those obtained by other agents with which it is coordinating. To avoid undesirable bias in relatively heterogeneous environments while effectively using relevant data to improve its estimations, the mechanism weighs the contributions of other agents' observations based on a real-time estimation of the level of similarity between each of these agents and itself. The "coordination autonomy" module of a coordination-manager system provided an empirical setting for evaluation. Simulation-based evaluations demonstrated that the proposed mechanism outperforms estimations based exclusively on an agent's own observations as well as estimations based on an unweighted aggregate of all other agents' observations.Engineering and Applied Science

Determining the Value of Information for Collaborative Multi-Agent Planning

This paper addresses the problem of computing the value of information in settings in which the people using an autonomous-agent system have access to information not directly available to the system itself. To know whether to interrupt a user for this information, the agent needs to determine its value. The fact that the agent typically does not know the exact information the user has and so must evaluate several alternative possibilities significantly increases the complexity of the value-of-information calculation. The paper addresses this problem as it arises in multi-agent task planning and scheduling with architectures in which information about the task schedule resides in a separate “scheduler” module. For such systems, calculating the value to overall agent performance of potential new information requires that the system component that interacts with the user query the scheduler. The cost of this querying and inter-module communication itself substantially affects system performance and must be taken into account. The paper provides a decision-theoretic algorithm for determining the value of information the system might acquire, query-reduction methods that decrease the number of queries the algorithm makes to the scheduler, and methods for ordering the queries to enable faster decision-making. These methods were evaluated in the context of a collaborative interface for an automated scheduling agent. Experimental results demonstrate the significant decrease achieved by using the query-reduction methods in the number of queries needed for reasoning about the value of information. They also show the ordering methods substantially increase the rate of value accumulation, enabling faster determination of whether to interrupt the user.Engineering and Applied Science

Effective Information Value Calculation for Interruption Management in Multi-Agent Scheduling.

This paper addresses the problem of deciding effectively whether to interrupt a teammate who may have information that is valuable for solving a collaborative scheduling problem. Two characteristics of multi-agent scheduling complicate the determination of the value of the teammate's information, and hence whether it exceeds the costs of an interruption. First, in many scheduling contexts, task and scheduling knowledge reside in a scheduler module which is external to the agent, and the agent must query that module to estimate the value to the solution of knowing a specific piece of information. Second, the agent does not know the specific information its teammate has, resulting in the need for it to repeatedly query the scheduler. Choosing the right sequence of queries to the scheduler may enable the agent to make an interruption decision sooner, thus saving query time and computational load for both the agent and the external system. This paper defines two new sequencing heuristics which enhance the efficiency of the querying process. It also introduces three metrics for measuring the efficiency of a query sequence. It presents extensive simulation-based evidence that the new heuristics significantly outperform previously proposed methods for determining the value of information a teammate has.Engineering and Applied Science

Problem restructuring for better decision making in recurring decision situations

This paper proposes the use of restructuring information about choices to improve the performance of computer agents on recurring sequentially dependent decisions. The intended situations of use for the restructuring methods it defines are website platforms such as electronic marketplaces in which agents typically engage in sequentially dependent decisions. With the proposed methods, such platforms can improve agents’ experience, thus attracting more customers to their sites. In sequentially-dependent-decisions settings, decisions made at one time may affect decisions made later; hence, the best choice at any point depends not only on the options at that point, but also on future conditions and the decisions made in them. This “problem restructuring” approach was tested on sequential economic search, which is a common type of recurring sequentially dependent decision-making problem that arises in a broad range of areas. The paper introduces four heuristics for restructuring the choices that are available to decision makers in economic search applications. Three of these heuristics are based on characteristics of the choices, not of the decision maker. The fourth heuristic requires information about a decision-makers prior decision-making, which it uses to classify the decision-maker. The classification type is used to choose the best of the three other heuristics. The heuristics were extensively tested on a large number of agents designed by different people with skills similar to those of a typical agent developer. The results demonstrate that the problem-restructuring approach is a promising one for improving the performance of agents on sequentially dependent decisions. Although there was a minor degradation in performance for a small portion of the agents, the overall and average individual performance improved substantially. Complementary experimentation with people demonstrated that the methods carry over, to some extent, also to human decision makers. Interestingly, the heuristic that adapts based on a decision-maker’s history achieved the best results for computer agents, but not for people.Engineering and Applied Science

Less is more: Restructuring decisions to improve agent search

In many settings and for various reasons, people fail to make optimal decisions. These factors also influence the agents people design to act on their behalf in such virtual environments as eCommerce and distributed operating systems, so that the agents also act sub-optimally despite their greater computational capabilities. In some decision-making situations it is theoretically possible to supply the optimal strategy to people or their agents, but this optimal strategy may be non-intuitive, and providing a convincing explanation of optimality may be complex. This paper explores an alternative approach to improving the performance of a decision-maker in such settings: the data on choices is manipulated to guide searchers to a strategy that is closer to optimal. This approach was tested for sequential search, which is a classical sequential decision-making problem with broad areas of applicability (e.g., product search, partnership search). The paper introduces three heuristics for manipulating choices, including one for settings in which repeated interaction or access to a decision-maker's past history is available. The heuristics were evaluated on a large population of computer agents, each of which embodies a search strategy programmed by a different person. Extensive tests on thousands of search settings demonstrate the promise of the problem-restructuring approach: despite a minor degradation in performance for a small portion of the population, the overall and average individual performance improve substantially. The heuristic that adapts based on a decision-maker's history achieved the best results.Engineering and Applied Science