Zinovi Rabinovich

The complexity of multiagent systems: the price of silence

In this work, we suggest representing multiagent systems using computational models, choosing, specifically, Multi-Prover Interactive Protocols to represent agent systems and the interactions occurring within them. This approach enables us to analyze complexity issues related to multiagent systems. We focus here on the complexity of coordination and study the possible sources of this complexity. We show that there are complexity bounds that cannot be lowered even when approximation techniques are applied.

Strategic Information Disclosure to People with Multiple Alternatives

In this article, we study automated agents that are designed to encourage humans to take some actions over others by strategically disclosing key pieces of information. To this end, we utilize the framework of persuasion games—a branch of game theory that deals with asymmetric interactions where one player (Sender) possesses more information about the world, but it is only the other player (Receiver) who can take an action. In particular, we use an extended persuasion model, where the Sender’s information is imperfect and the Receiver has more than two alternative actions available. We design a computational algorithm that, from the Sender’s standpoint, calculates the optimal information disclosure rule. The algorithm is parameterized by the Receiver’s decision model (i.e., what choice he will make based on the information disclosed by the Sender) and can be retuned accordingly. We then provide an extensive experimental study of the algorithm’s performance in interactions with human Receivers. First, we consider a fully rational (in the Bayesian sense) Receiver decision model and experimentally show the efficacy of the resulting Sender’s solution in a routing domain. Despite the discrepancy in the Sender’s and the Receiver’s utilities from each of the Receiver’s choices, our Sender agent successfully persuaded human Receivers to select an option more beneficial for the agent. Dropping the Receiver’s rationality assumption, we introduce a machine learning procedure that generates a more realistic human Receiver model. We then show its significant benefit to the Sender solution by repeating our routing experiment. To complete our study, we introduce a second (supply--demand) experimental domain and, by contrasting it with the routing domain, obtain general guidelines for a Sender on how to construct a Receiver model.

Multiagent coordination by Extended Markov Tracking

We present here Extended Markov Tracking (EMT), a computationally tractable method for the online estimation of Markovian system dynamics, along with experimental support for its successful contribution to a specific control architecture. The control architecture leverages EMT to simultaneously track and correct system dynamics.Using a widespread extension of the Markovian environment model to multiagent systems, we provide an application of EMT-based control to multiagent coordination. The resulting coordinated action algorithm, in contrast to alternative approaches, does not eliminate interference among agents, but rather exploits it for purposes of synchronization and implicit information transfer. This information transfer enables the algorithm to be computationally tractable. Experiments are presented that demonstrate the effectiveness of EMT-based control for multiagent coordination in stochastic environments.

On the response of EMT-based control to interacting targets and models

A novel control mechanism was recently introduced based on Extended Markov Tracking (EMT) [9, 10]. In this paper, we present a study of its response to multiple interacting control goals. We show a simple extension that can be integrated into EMT-based control, and which provides it with the ability to handle several behavioral targets. Experimental support for the validity of this extension is provided. We also describe an experiment with a simulated robot, where EMT-based controllers interact and interfere indirectly via the environment. Experiments support the resilience of multiagent EMT-based team control to potential conflicts that may appear within a team.

Beyond Plurality: Truth-Bias in Binary Scoring Rules

It is well known that standard game-theoretic approaches to voting mechanisms lead to a multitude of Nash Equilibria NE, many of which are counter-intuitive. We focus on truth-biased voters, a model recently proposed to avoid such issues. The model introduces an incentive for voters to be truthful when their vote is not pivotal. This is a powerful refinement, and recent simulations reveal that the surviving equilibria tend to have desirable properties. However, truth-bias has been studied only within the context of plurality, which is an extreme example of k-approval rules with

Haste makes waste: a case to favour voting bots

k=1

Analysis of equilibria in iterative voting schemes

. We undertake an equilibrium analysis of the complete range of k-approval. Our analysis begins with the veto rule, the other extreme point of k-approval, where each ballot approves all candidates but one. We identify several crucial properties of pure NE for truth-biased veto. These properties show a clear distinction from the setting of truth-biased plurality. We proceed by establishing that deciding on the existence of NE in truth-biased veto is an NP-hard problem. We also characterise a tight in a certain sense subclass of instances for which the existence of a NE can be decided in poly-time. Finally, we study analogous questions for general k-approval rules.

Computing pure Bayesian-Nash equilibria in games with finite actions and continuous types

In this paper we introduce Dynamics Based Control (DBC), an approach to planning and control of an agent in stochastic environments. Unlike existing approaches, which seek to optimize expected rewards (e.g., in Partially Observable Markov Decision Problems (POMDPs)), DBC optimizes system behavior towards specified system dynamics. We show that a recently developed planning and control approach, Extended Markov Tracking (EMT) is an instantiation of DBC. EMT employs greedy action selection to provide an efficient control algorithm in Markovian environments. We exploit this efficiency in a set of experiments that applied multi-target EMT to a class of area-sweeping problems (searching for moving targets). We show that such problems can be naturally defined and efficiently solved using the DBC framework, and its EMT instantiation.