Landmark-based approaches for goal recognition as planning

Pereira, Ramon Fraga; Oren, Nir; Meneguzzi, Felipe

doi:10.1016/j.artint.2019.103217

Cited by 42 publications

(47 citation statements)

References 34 publications

(95 reference statements)

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“…Several works followed that approach [ 58 , 71 , 72 , 73 , 74 ] by using various automated planning techniques to analyze and solve goal or plan recognition problems. Its basic ideas have also been applied in new problems, such as goal recognition design [ 34 ] and deceptive path-planning [ 5 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Single Real Goal, Magnitude-Based Deceptive Path-Planning

Zeng

Qin

et al. 2020

Entropy

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Deceptive path-planning is the task of finding a path so as to minimize the probability of an observer (or a defender) identifying the observed agent’s final goal before the goal has been reached. It is one of the important approaches to solving real-world challenges, such as public security, strategic transportation, and logistics. Existing methods either cannot make full use of the entire environments’ information, or lack enough flexibility for balancing the path’s deceptivity and available moving resource. In this work, building on recent developments in probabilistic goal recognition, we formalized a single real goal magnitude-based deceptive path-planning problem followed by a mixed-integer programming based deceptive path maximization and generation method. The model helps to establish a computable foundation for any further imposition of different deception concepts or strategies, and broadens its applicability in many scenarios. Experimental results showed the effectiveness of our methods in deceptive path-planning compared to the existing one.

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Section: Background and Related Workmentioning

confidence: 99%

Single Real Goal, Magnitude-Based Deceptive Path-Planning

Zeng

Qin

et al. 2020

Entropy

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“…, α is a normalizing constant across all goals, and β a positive constant which captures a 'soft rationality' assumption. Several works followed this approach [25,[40][41][42][43][44] by using various automated planning techniques to analyze and solve goal or plan recognition problems. Its basic ideas have also been applied in new problems such as Goal Recognition Design [6], Deceptive Path-Planning [45], etc.…”

Section: Probabilistic Goal Recognitionmentioning

confidence: 99%

Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric

Yin

2019

Entropy

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Recent research has found situations where the identification of agent goals could be purposefully controlled, either by changing the underlying environment to make it easier, or exploiting it during agent planning to delay the opponent’s goal recognition. The paper tries to answer the following questions: what kinds of actions contain less information and more uncertainty about the agent’s real goal, and how to describe this uncertainty; what is the best way to control the process of goal identification. Our contribution is the introduction of a new measure we call relative goal uncertainty (rgu) with which we assess the goal-related information that each action contains. The rgu is a relative value associated with each action and represents the goal uncertainty quantified by information entropy after the action is taken compared to other executable ones in each state. After that, we show how goal vagueness could be controlled either for one side or for both confronting sides, and formulate this goal identification control problem as a mixed-integer programming problem. Empirical evaluation shows the effectiveness of the proposed solution in controlling goal identification process.

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“…Through the deployment of numerous IoT sensors, smart environments can attempt to recognise the goal of a human from the actions they perform, and thus become more context-aware. Despite recent advances in Goal Recognition (GR) techniques [1,2], a person’s goal often cannot be determined until their plan nears completion. This is because the plans to reach different goals can initially be identical.…”

Section: Introductionmentioning

confidence: 99%

“…Second, the possibility of performing an action can be removed, e.g., a barrier or ornament can be placed to prevent a human from navigating between two positions. The resulting environment design potentially improves the accuracy of GR approaches, such as [7,8,9], and requires fewer distinct actions to be detected. In some cases, this reduction could lead to fewer, cheaper or less privacy invasive sensors being deployed in context-aware smart environments.…”

Section: Introductionmentioning

confidence: 99%

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Action Graphs for Performing Goal Recognition Design on Human-Inhabited Environments

Harman

Simoens

2019

Sensors

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Goal recognition is an important component of many context-aware and smart environment services; however, a person’s goal often cannot be determined until their plan nears completion. Therefore, by modifying the state of the environment, our work aims to reduce the number of observations required to recognise a human’s goal. These modifications result in either: Actions in the available plans being replaced with more distinctive actions; or removing the possibility of performing some actions, so humans are forced to take an alternative (more distinctive) plan. In our solution, a symbolic representation of actions and the world state is transformed into an Action Graph, which is then traversed to discover the non-distinctive plan prefixes. These prefixes are processed to determine which actions should be replaced or removed. For action replacement, we developed an exhaustive approach and an approach that shrinks the plans then reduces the non-distinctive plan prefixes, namely Shrink–Reduce. Exhaustive is guaranteed to find the minimal distinctiveness but is more computationally expensive than Shrink–Reduce. These approaches are compared using a test domain with varying amounts of goals, variables and values, and a realistic kitchen domain. Our action removal method is shown to increase the distinctiveness of various grid-based navigation problems, with a width/height ranging from 4 to 16 and between 2 and 14 randomly selected goals, by an average of 3.27 actions in an average time of 4.69 s, whereas a state-of-the-art approach often breaches a 10 min time limit.

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Landmark-based approaches for goal recognition as planning

Cited by 42 publications

References 34 publications

Single Real Goal, Magnitude-Based Deceptive Path-Planning

Single Real Goal, Magnitude-Based Deceptive Path-Planning

Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric

Action Graphs for Performing Goal Recognition Design on Human-Inhabited Environments

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