`Why didn't you allocate this task to them?' Negotiation-Aware Task Allocation and Contrastive Explanation Generation

Task-allocation is an important problem in multi-agent systems. It becomes more challenging when the team-members are humans with imperfect knowledge about their teammates' costs and the overall performance metric. While distributed task-allocation methods let the team-members engage in iterative dialog to reach a consensus, the process can take a considerable amount of time and communication. On the other hand, a centralized method that simply outputs an allocation may result in discontented human team-members who, due to their imperfect knowledge and limited computation capabilities, perceive the allocation to be unfair. To address these challenges, we propose a centralized Artificial Intelligence Task Allocation (AITA) that simulates a negotiation and produces a negotiation-aware task allocation that is fair. If a team-member is unhappy with the proposed allocation, we allow them to question the proposed allocation using a counterfactual. By using parts of the simulated negotiation, we are able to provide contrastive explanations that providing minimum information about other's costs to refute their foil. With human studies, we show that (1) the allocation proposed using our method does indeed appear fair to the majority, and (2) when a counterfactual is raised, explanations generated are easy to comprehend and convincing. Finally, we empirically study the effect of different kinds of incompleteness on the explanation-length and find that underestimation of a teammate's costs often increases it.Comment: First two authors are equal contributor

Team Integration

We leverage theoretical advances and the multi-user nature of \emph{argumentation}. The overall contributions of our work are as follows. We model the schema matching network and the reconciliation process, where we relate the experts' assertions and the constraints of the matching network to an \emph{argumentation framework}. Our representation not only captures the experts' belief and their explanations, but also enables to reason about these captured inputs. On top of this representation, we develop support techniques for experts to detect conflicts in a set of their assertions. Then we guide the conflict resolution by offering two primitives: \emph{conflict-structure interpretation} and \emph{what-if analysis}. While the former presents meaningful interpretations for the conflicts and various heuristic metrics, the latter can greatly help the experts to understand the consequences of their own decisions as well as those of others. Last but not least, we implement an argumentation-based negotiation support tool for schema matching (ArgSM), which realizes our methods to help the experts in the collaborative task

Negotiation Based Resource Allocation to Control Information Diffusion

Study of diffusion or propagation of information over a network of connected entities play a vital role in understanding and analyzing the impact of such diffusion, in particular, in the context of epidemiology, and social and market sciences. Typical concerns addressed by these studies are to control the diffusion such that influence is maximally (in case of opinion propagation) or minimally (in case of infectious disease) felt across the network. Controlling diffusion requires deployment of resources and often availability of resources are socio-economically constrained. In this context, we propose an agent-based framework for resource allocation, where agents operate in a cooperative environment and each agent is responsible for identifying and validating control strategies in a network under its control. The framework considers the presence of a central controller that is responsible for negotiating with the agents and allocate resources among the agents. Such assumptions replicates real-world scenarios, particularly in controlling infection spread, where the resources are distributed by a central agency (federal govt.) and the deployment of resources are managed by a local agency (state govt.). If there exists an allocation that meets the requirements of all the agents, our framework is guaranteed to find one such allocation. While such allocation can be obtained in a blind search methods (such as checking the minimum number of resources required by each agent or by checking allocations between each pairs), we show that considering the responses from each agent and considering allocation among all the agents results in a “negotiation” based technique that converges to a solution faster than the brute force methods. We evaluated our framework using data publicly available from Stanford Network Analysis Project to simulate different types of networks for each agents

An Efficient Protocol for Negotiation over Multiple Indivisible Resources

We study the problem of autonomous agents negotiating the allocation of multiple indivisible resources. It is difficult to reach optimal outcomes in bilateral or multi-lateral negotiations over multiple resources when the agents ’ preferences for the resources are not common knowledge. Selfinterested agents often end up negotiating inefficient agreements in such situations. We present a protocol for negotiation over multiple indivisible resources which can be used by rational agents to reach efficient outcomes. Our proposed protocol enables the negotiating agents to identify efficient solutions using systematic distributed search that visits only a subspace of the whole solution space.

An Efficient Protocol for Negotiation over Multiple Indivisible Resources

We study the problem of autonomous agents negotiating the allocation of multiple indivisible resources. It is difficult to reach optimal outcomes in bilateral or multi-lateral negotiations over multiple resources when the agents ’ preferences for the resources are not common knowledge. Selfinterested agents often end up negotiating inefficient agreements in such situations. We present a protocol for negotiation over multiple indivisible resources which can be used by rational agents to reach efficient outcomes. Our proposed protocol enables the negotiating agents to identify efficient solutions using systematic distributed search that visits only a subspace of the whole solution space.