Multi-agent Plan Recognition Research Articles

Recognizing Multi-Agent Plans When Action Models and Team Plans Are Both Incomplete

Multi-Agent Plan Recognition (MAPR) aims to recognize team structures (which are composed of team plans) from the observed team traces (action sequences) of a set of intelligent agents. In this article, we introduce the problem formulation of MAPR based on partially observed team traces, and present a weighted MAX-SAT–based framework to recognize multi-agent plans from partially observed team traces with the help of two types of auxiliary knowledge to help recognize multi-agent plans, i.e., a library ofincompleteteam plans and a set ofincompleteaction models. Our framework functions with two phases. We first build a set ofhardconstraints that encode the correctness property of the team plans, and a set ofsoftconstraints that encode the optimal utility property of team plans based on the input team trace, incomplete team plans, and incomplete action models. After that, we solve all of the constraints using a weighted MAX-SAT solver and convert the solution to a set of team plans that bestexplainthe structure of the observed team trace. We empirically exhibit both effectiveness and efficiency of our framework in benchmark domains from International Planning Competition (IPC).

The complexity of multi-agent plan recognition

Multi-agent plan recognition (MAPR) seeks to identify the dynamic team structures and team plans from observations of the action sequences of a set of intelligent agents, based on a library of known team plans (plan library), and an evaluation function. It has important applications in decision support, team work, analyzing data from automated monitoring, surveillance, and intelligence analysis in general. We introduce a general model for MAPR that accommodates different representations of the plan library, and includes single agent plan recognition as a special case. Thus it provides an ideal substrate to investigate and contrast the complexities of single and multi-agent plan recognition. Using this model we generate theoretical insights on hardness, with practical implications. A key feature of these results is that they are baseline, i.e., the polynomial solvability results are given in terms of a compact and expressive plan language (context free language), while the hardness results are given in terms of a less compact language. Consequently the hardness results continue to hold in virtually all realistic plan languages, while the polynomial solvability results extend to the subsets of the context free plan language. In particular, we show that MAPR is in P (polynomial in the size of the plan library and the observation trace) if the number of agents is fixed (in particular 1) but NP-complete otherwise. If the number of agents is a variable, then even the one step MAPR problem is NP-complete. While these results pertain to abduction, we also investigate a related question: adaptation, i.e., the problem of refining the evaluation function based on feedback. We show that adaptation is also NP-hard for a variable number of agents, but easy for a single agent. These results establish a clear distinction between the hardnesses of single and multi-agent plan recognition even in idealized settings, indicating the necessity of a fundamentally different set of techniques for the latter.

Activity Recognition for Dynamic Multi-Agent Teams

This article addresses the problem of activity recognition for dynamic, physically embodied agent teams. We define team activity recognition as the process of identifying team behaviors from traces of agent positions over time; for many physical domains, military or athletic, coordinated team behaviors create distinctive spatio-temporal patterns that can be used to identify low-level action sequences. This article focuses on the novel problem of recovering agent-to-team assignments for complex team tasks where team composition, the mapping of agents into teams, changes over time. We suggest two methods for improving the computational efficiency of the multi-agent plan recognition process in these cases of changing team composition; our proposed approach is robust to sensor observation noise and errors in behavior classification.

Branch and Price for Multi-Agent Plan Recognition

The problem of identifying the (dynamic) team structures and team behaviors from the observed activities of multiple agents is called Multi-Agent Plan Recognition (MAPR). We extend a recent formalization of this problem to accommodate a compact, partially ordered, multi-agent plan language, as well as complex plan execution models — particularly plan abandonment and activity interleaving. We adopt a branch and price approach to solve MAPR in such a challenging setting, and fully instantiate the (generic) pricing problem for MAPR. We show experimentally that this approach outperforms a recently proposed hypothesis pruning algorithm in two domains: multi-agent blocks word, and intrusion detection. The key benefit of the branch and price approach is its ability to grow the necessary component (occurrence) space from which the hypotheses are constructed, rather than begin with a fully enumerated component space that has an intractable size, and search it with pruning. Our formulation of MAPR has the broad objective of bringing mature Operations Research methodologies to bear upon MAPR, envisaged to have a similar impact as mature SAT-solvers had on planning.

Multi-Agent Plan Recognition: Formalization and Algorithms

Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observations of the activity-sequences of a set of intelligent agents, based on a library of known team-activities (plan library). It has important applications in analyzing data from automated monitoring, surveillance, and intelligence analysis in general. In this paper, we formalize MAPR using a basic model that explicates the cost of abduction in single agent plan recognition by "flattening" or decompressing the (usually compact, hierarchical) plan library. We show that single-agent plan recognition with a decompressed library can be solved in time polynomial in the input size, while it is known that with a compressed (by partial ordering constraints) library it is NP-complete. This leads to an important insight: that although the compactness of the plan library plays an important role in the hardness of single-agent plan recognition (as recognized in the existing literature), that is not the case with multiple agents. We show, for the first time, that MAPR is NP-complete even when the (multi-agent) plan library is fully decompressed. As with previous solution approaches, we break the problem into two stages: hypothesis generation and hypothesis search. We show that Knuth's ``Algorithm X'' (with the efficient ``dancing links'' representation) is particularly suited for our model, and can be adapted to perform a branch and bound search for the second stage, in this model. We show empirically that this new approach leads to significant pruning of the hypothesis space in MAPR.

Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach

Recent years are seeing an increasing need for on-line monitoring of teams of cooperating agents, e.g., for visualization, or performance tracking. However, in monitoring deployed teams, we often cannot rely on the agents to always communicate their state to the monitoring system. This paper presents a non-intrusive approach to monitoring by 'overhearing', where the monitored team's state is inferred (via plan-recognition) from team-members' *routine* communications, exchanged as part of their coordinated task execution, and observed (overheard) by the monitoring system. Key challenges in this approach include the demanding run-time requirements of monitoring, the scarceness of observations (increasing monitoring uncertainty), and the need to scale-up monitoring to address potentially large teams. To address these, we present a set of complementary novel techniques, exploiting knowledge of the social structures and procedures in the monitored team: (i) an efficient probabilistic plan-recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting knowledge of the team's social behavior to predict future observations during execution (reducing monitoring uncertainty); and (iii) monitoring algorithms that trade expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any number of agents and their different activities to be represented in a single coherent entity. We present an empirical evaluation of these techniques, in combination and apart, in monitoring a deployed team of agents, running on machines physically distributed across the country, and engaged in complex, dynamic task execution. We also compare the performance of these techniques to human expert and novice monitors, and show that the techniques presented are capable of monitoring at human-expert levels, despite the difficulty of the task.