435 research outputs found
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
A key challenge in multi-robot and multi-agent systems is generating
solutions that are robust to other self-interested or even adversarial parties
who actively try to prevent the agents from achieving their goals. The
practicality of existing works addressing this challenge is limited to only
small-scale synchronous decision-making scenarios or a single agent planning
its best response against a single adversary with fixed, procedurally
characterized strategies. In contrast this paper considers a more realistic
class of problems where a team of asynchronous agents with limited observation
and communication capabilities need to compete against multiple strategic
adversaries with changing strategies. This problem necessitates agents that can
coordinate to detect changes in adversary strategies and plan the best response
accordingly. Our approach first optimizes a set of stratagems that represent
these best responses. These optimized stratagems are then integrated into a
unified policy that can detect and respond when the adversaries change their
strategies. The near-optimality of the proposed framework is established
theoretically as well as demonstrated empirically in simulation and hardware
Can bounded and self-interested agents be teammates? Application to planning in ad hoc teams
Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of self-interested decision-making frameworks. Agents engaged in individual decision making in multiagent settings face the task of having to reason about other agents’ actions, which may in turn involve reasoning about others. An established approximation that operationalizes this approach is to bound the infinite nesting from below by introducing level 0 models. For the purposes of this study, individual, self-interested decision making in multiagent settings is modeled using interactive dynamic influence diagrams (I-DID). These are graphical models with the benefit that they naturally offer a factored representation of the problem, allowing agents to ascribe dynamic models to others and reason about them. We demonstrate that an implication of bounded, finitely-nested reasoning by a self-interested agent is that we may not obtain optimal team solutions in cooperative settings, if it is part of a team. We address this limitation by including models at level 0 whose solutions involve reinforcement learning. We show how the learning is integrated into planning in the context of I-DIDs. This facilitates optimal teammate behavior, and we demonstrate its applicability to ad hoc teamwork on several problem domains and configurations
Structure in the Value Function of Two-Player Zero-Sum Games of Incomplete Information
Zero-sum stochastic games provide a rich model for competitive decision
making. However, under general forms of state uncertainty as considered in the
Partially Observable Stochastic Game (POSG), such decision making problems are
still not very well understood. This paper makes a contribution to the theory
of zero-sum POSGs by characterizing structure in their value function. In
particular, we introduce a new formulation of the value function for zs-POSGs
as a function of the "plan-time sufficient statistics" (roughly speaking the
information distribution in the POSG), which has the potential to enable
generalization over such information distributions. We further delineate this
generalization capability by proving a structural result on the shape of value
function: it exhibits concavity and convexity with respect to appropriately
chosen marginals of the statistic space. This result is a key pre-cursor for
developing solution methods that may be able to exploit such structure.
Finally, we show how these results allow us to reduce a zs-POSG to a
"centralized" model with shared observations, thereby transferring results for
the latter, narrower class, to games with individual (private) observations
Adaptive Information Gathering via Imitation Learning
In the adaptive information gathering problem, a policy is required to select
an informative sensing location using the history of measurements acquired thus
far. While there is an extensive amount of prior work investigating effective
practical approximations using variants of Shannon's entropy, the efficacy of
such policies heavily depends on the geometric distribution of objects in the
world. On the other hand, the principled approach of employing online POMDP
solvers is rendered impractical by the need to explicitly sample online from a
posterior distribution of world maps.
We present a novel data-driven imitation learning framework to efficiently
train information gathering policies. The policy imitates a clairvoyant oracle
- an oracle that at train time has full knowledge about the world map and can
compute maximally informative sensing locations. We analyze the learnt policy
by showing that offline imitation of a clairvoyant oracle is implicitly
equivalent to online oracle execution in conjunction with posterior sampling.
This observation allows us to obtain powerful near-optimality guarantees for
information gathering problems possessing an adaptive sub-modularity property.
As demonstrated on a spectrum of 2D and 3D exploration problems, the trained
policies enjoy the best of both worlds - they adapt to different world map
distributions while being computationally inexpensive to evaluate.Comment: Robotics Science and Systems, 201
Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs
Interactive partially observable Markov decision processes (I-POMDP) provide
a formal framework for planning for a self-interested agent in multiagent
settings. An agent operating in a multiagent environment must deliberate about
the actions that other agents may take and the effect these actions have on the
environment and the rewards it receives. Traditional I-POMDPs model this
dependence on the actions of other agents using joint action and model spaces.
Therefore, the solution complexity grows exponentially with the number of
agents thereby complicating scalability. In this paper, we model and extend
anonymity and context-specific independence -- problem structures often present
in agent populations -- for computational gain. We empirically demonstrate the
efficiency from exploiting these problem structures by solving a new multiagent
problem involving more than 1,000 agents.Comment: 8 page article plus two page appendix containing proofs in
Proceedings of 25th International Conference on Autonomous Planning and
Scheduling, 201
A Framework for Sequential Planning in Multi-Agent Settings
This paper extends the framework of partially observable Markov decision
processes (POMDPs) to multi-agent settings by incorporating the notion of agent
models into the state space. Agents maintain beliefs over physical states of
the environment and over models of other agents, and they use Bayesian updates
to maintain their beliefs over time. The solutions map belief states to
actions. Models of other agents may include their belief states and are related
to agent types considered in games of incomplete information. We express the
agents autonomy by postulating that their models are not directly manipulable
or observable by other agents. We show that important properties of POMDPs,
such as convergence of value iteration, the rate of convergence, and piece-wise
linearity and convexity of the value functions carry over to our framework. Our
approach complements a more traditional approach to interactive settings which
uses Nash equilibria as a solution paradigm. We seek to avoid some of the
drawbacks of equilibria which may be non-unique and do not capture
off-equilibrium behaviors. We do so at the cost of having to represent, process
and continuously revise models of other agents. Since the agents beliefs may be
arbitrarily nested, the optimal solutions to decision making problems are only
asymptotically computable. However, approximate belief updates and
approximately optimal plans are computable. We illustrate our framework using a
simple application domain, and we show examples of belief updates and value
functions
Iterative Online Planning in Multiagent Settings with Limited Model Spaces and PAC Guarantees
Methods for planning in multiagent settings often model other agents ’ possible behaviors. However, the space of these models – whether these are policy trees, finite-state controllers or inten-tional models – is very large and thus arbitrarily bounded. This may exclude the true model or the optimal model. In this paper, we present a novel iterative algorithm for online planning that consid-ers a limited model space, updates it dynamically using data from interactions, and provides a provable and probabilistic bound on the approximation error. We ground this approach in the context of graphical models for planning in partially observable multiagent settings – interactive dynamic influence diagrams. We empirically demonstrate that the limited model space facilitates fast solutions and that the true model often enters the limited model space
Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling
State-of-the-art approaches to partially observable planning like POMCP are
based on stochastic tree search. While these approaches are computationally
efficient, they may still construct search trees of considerable size, which
could limit the performance due to restricted memory resources. In this paper,
we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory
bounded approach to open-loop planning in large POMDPs, which optimizes a fixed
size stack of Thompson Sampling bandits. We empirically evaluate POSTS in four
large benchmark problems and compare its performance with different tree-based
approaches. We show that POSTS achieves competitive performance compared to
tree-based open-loop planning and offers a performance-memory tradeoff, making
it suitable for partially observable planning with highly restricted
computational and memory resources.Comment: Presented at AAAI 201
Reinforcement learning for efficient network penetration testing
Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-standard, composite and resource-consuming despite the use of evolving tools. In this paper, we propose and evaluate an AI-based pentesting system which makes use of machine learning techniques, namely reinforcement learning (RL) to learn and reproduce average and complex pentesting activities. The proposed system is named Intelligent Automated Penetration Testing System (IAPTS) consisting of a module that integrates with industrial PT frameworks to enable them to capture information, learn from experience, and reproduce tests in future similar testing cases. IAPTS aims to save human resources while producing much-enhanced results in terms of time consumption, reliability and frequency of testing. IAPTS takes the approach of modeling PT environments and tasks as a partially observed Markov decision process (POMDP) problem which is solved by POMDP-solver. Although the scope of this paper is limited to network infrastructures PT planning and not the entire practice, the obtained results support the hypothesis that RL can enhance PT beyond the capabilities of any human PT expert in terms of time consumed, covered attacking vectors, accuracy and reliability of the outputs. In addition, this work tackles the complex problem of expertise capturing and re-use by allowing the IAPTS learning module to store and re-use PT policies in the same way that a human PT expert would learn but in a more efficient way
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