Action-Model Based Multi-agent Plan Recognition (original) (raw)

Multi-Agent Plan Recognition with Partial Team Traces and Plan Libraries

Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observed activity sequences (team traces) of a set of intelligent agents, based on a library of known team activity sequences (team plans). Previous MAPR systems require that team traces and team plans are fully observed. In this paper we relax this constraint, i.e., team traces and team plans are allowed to be partial. This is an important task in applying MAPR to real-world domains, since in many applications it is often difficult to collect full team traces or team plans due to environment limitations, e.g., military operation. This is also a hard problem since the information available is limited. We propose a novel approach to recognizing team plans from partial team traces and team plans. We encode the MAPR problem as a satisfaction problem and solve the problem using a state-of-the-art weighted MAX-SAT solver. We empirically show that our algorithm is both effective and efficient.

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

ACM Transactions on Intelligent Systems and Technology, 2019

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...

Robust and efficient plan recognition for dynamic multi-agent teams

2008

This paper addresses the problem of plan recognition for multiagent teams. Complex multi-agent tasks typically require dynamic teams where the team membership changes over time. Teams split into subteams to work in parallel, merge with other teams to tackle more demanding tasks, and disband when plans are completed. We introduce a new multi-agent plan representation that explicitly encodes dynamic team membership and demonstrate the suitability of this formalism for plan recognition. From our multi-agent plan representation, we extract local temporal dependencies that dramatically prune the hypothesis set of potentially-valid team plans. The reduced plan library can be efficiently processed to obtain the team state history. Naive pruning can be inadvisable when low-level observations are unreliable due to sensor noise and classification errors. In such conditions, we eschew pruning in favor of prioritization and show how our scheme can be extended to rank-order the hypotheses. Experiments show that this robust pre-processing approach ranks the correct plan within the top 10%, even under conditions of severe noise.

An AI Planning-Based Approach to the Multi-Agent Plan Recognition Problem

2018

Multi-Agent Plan Recognition (MAPR) is the problem of inferring the goals and plans of multiple agents given a set of observations. While previous MAPR approaches have largely focused on recognizing team structures and behaviors, given perfect and complete observations, in this paper, we address potentially unreliable observations and temporal actions. We propose a multi-step compilation technique that enables the use of AI planning for the computation of the probability distributions of plans and goals, given observations. We present results of an experimental evaluation on a novel set of benchmarks, using several temporal and diverse planners.

Activity Recognition for Dynamic Multi-Agent Teams

ACM Transactions on Intelligent Systems and Technology, 2011

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.

Software Agents for Incremental, Team-Oriented Activity and Plan Recognition

2010

Abstract—Activity and plan recognition are well developed research fields, where much work has been done. There has been, however, almost no effort in combining both to create intelligent software that can effectively support the decisionmaking process faced by human teams enacting a joint plan. If intelligent software assistants are to offer helpful and meaningful support, and interact effectively in human-agent teams, then a team-oriented, incremental and distributed approach has to be considered.

Activity Recognition for Physically-Embodied Agent Teams

2005

This thesis focuses on the problem of activity recognition for physicallyembodied agent teams. We define team activity recognition as the process of identifying team behaviors from traces of agents' positions and orientations as they evolve over time; the goal is to completely annotate agent traces with: 1) the correct sequence of low-level actions performed by each agent; 2) an assignment of agents to teams and subteams; 3) the set of team plans consistent with the observed sequence. Activity traces are gathered from teams of humans or agents performing military tasks in urban environments. Team behavior annotations can be used for a wide variety of applications including virtual training environments, visual monitoring systems, and commentator agents.

Learning action models for multi-agent planning

Adaptive Agents and Multi-Agents Systems, 2011

In multi-agent planning environments, action models for each agent must be given as input. However, creating such action models by hand is difficult and time-consuming, because it requires formally representing the complex relationships among different objects in the environment. The problem is compounded in multi-agent environments where agents can take more types of actions. In this paper, we present an algorithm to learn action models for multi-agent planning systems from a set of input plan traces. Our learning algorithm Lammas automatically generates three kinds of constraints: (1) constraints on the interactions between agents, (2) constraints on the correctness of the action models for each individual agent, and (3) constraints on actions themselves. Lammas attempts to satisfy these constraints simultaneously using a weighted maximum satisfiability model known as MAX-SAT, and converts the solution into action models. We believe this to be one of the first learning algorithms to learn action models in the context of multi-agent planning environments. We empirically demonstrate that Lammas performs effectively and efficiently in several planning domains.

Simultaneous team assignment and behavior recognition from spatio-temporal agent traces

2006

This paper addresses the problem of activity recognition for 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 paper 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. Without a priori knowledge of current team assignments, the behavior recognition problem is challenging since behaviors are characterized by the aggregate motion of the entire team and cannot generally be determined by observing the movements of a single agent in isolation. To handle this problem, we introduce a new algorithm, Simultaneous Team Assignment and Behavior Recognition (STABR), that generates behavior annotations from spatio-temporal agent traces. The proposed approach is able to perform accurate team behavior recognition without an exhaustive search over the combinatorial space of potential team assignments, as demonstrated on several scenarios of simulated military maneuvers.