No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Combining Symbolic and Statistical Knowledge for Goal Recognition in Smart Home Environments
... In this paper, we focus on observed action sequences for the theoretical discussion of the goal recognition problem. This task is relevant in many application domains like crime detection [6], pervasive computing [19,5], or traffic monitoring [12]. Previous goal recognition systems often rely on the principle of Plan Recognition As Planning (PRAP) and, hence, utilize concepts and algorithms from the classical planning community to solve the goal recognition problem [13,14,17,1]. ...
... Afterward, Algorithm 1 calculates the heuristic value for all goals (lines 10-12). As a last step, from the calculated heuristic values, Algorithm 1 selects the goals that have the maximum value (lines [13][14][15][16][17][18][19]: ...
... C is the set of facts for which the algorithm samples supporting ac-tions, f ound is a set that keeps track of the facts already supported, and sups is the set of supporting actions already selected for the current sample. The search for supporting actions for a fact p occurs in lines [13][14][15][16][17][18][19][20][21][22]. For this search, the algorithm starts at the first action level of the RPG, which ensures that supporters closer to s0 are found first. ...
We present a new approach to goal recognition that involves comparing observed facts with their expected probabilities. These probabilities depend on a specified goal g and initial state s0. Our method maps these probabilities and observed facts into a real vector space to compute heuristic values for potential goals. These values estimate the likelihood of a given goal being the true objective of the observed agent. As obtaining exact expected probabilities for observed facts in an observation sequence is often practically infeasible, we propose and empirically validate a method for approximating these probabilities. Our empirical results show that the proposed approach offers improved goal recognition precision compared to state-of-the-art techniques while reducing computational complexity.
... Goal recognition is the task of recognizing the goal(s) of an observed agent from a possibly incomplete sequence of actions executed by an observed agent. This task is relevant in many real-world application domains like crime detection [5], pervasive computing [19], [4], or traffic monitoring [12]. State-of-the-art goal recognition systems often rely on the principle of Plan Recognition As Planning (PRAP) and hence, utilize classical planning systems to solve the goal recognition problem [13], [14], [17], [1]. ...
Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible. However, many early approaches in the area of Plan Recognition As Planning, require quite large amounts of computation time to calculate a solution. Mainly to address this issue, recently, Pereira et al. developed an approach that is based on planning landmarks and is much more computationally efficient than previous approaches. However, the approach, as proposed by Pereira et al., also uses trivial landmarks (i.e., facts that are part of the initial state and goal description are landmarks by definition). In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach. The empirical results show that omitting initial state landmarks for goal recognition improves goal recognition performance.
... This paper is based on simulation, but the proposed algorithm has more realworld application scenarios. The algorithm provides ideas not only for adversarial game scenarios, but also for other multi-agent scenarios involving complex temporal logic tasks such as goal recognition in smart home environments [31,32]. In the future, we will apply the algorithm to plan recognition in realistic real-time strategy games. ...
This paper studies the plan recognition problem of multi-agent systems with temporal logic tasks. The high-level temporal tasks are represented as linear temporal logic (LTL). We present a probabilistic plan recognition algorithm to predict the future goals and identify the temporal logic tasks of the agent based on the observations of their states and actions. We subsequently build a plan library composed of Nondeterministic Bu¨chi Automation to model the temporal logic tasks. We also propose a Boolean matrix generation algorithm to map the plan library to multi-agent trajectories and a task recognition algorithm to parse the Boolean matrix. Then, the probability calculation formula is proposed to calculate the posterior goal probability distribution, and the cold start situation of the plan recognition is solved using the Bayes formula. Finally, we validate the proposed algorithm via extensive comparative simulations.
Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible. However, many early approaches in the area of Plan Recognition As Planning, require quite large amounts of computation time to calculate a solution. Mainly to address this issue, recently, Pereira et al. developed an approach that is based on planning landmarks and is much more computationally efficient than previous approaches. However, the approach, as proposed by Pereira et al., considers trivial landmarks (i.e., facts that are part of the initial state and goal description are landmarks by definition) for goal recognition. In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach. The empirical results show that omitting initial state landmarks for goal recognition improves goal recognition performance.
Wellbeing is often affected by health-related conditions. Among them are nutrition-related health conditions, which can significantly decrease the quality of life. We envision a system that monitors the kitchen activities of patients and that based on the detected eating behaviour could provide clinicians with indicators for improving a patient’s health. To be successful, such system has to reason about the person’s actions and goals. To address this problem, we introduce a symbolic behaviour recognition approach, called Computational Causal Behaviour Models (CCBM). CCBM combines symbolic representation of person’s behaviour with probabilistic inference to reason about one’s actions, the type of meal being prepared, and its potential health impact. To evaluate the approach, we use a cooking dataset of unscripted kitchen activities, which contains data from various sensors in a real kitchen. The results show that the approach is able to reason about the person’s cooking actions. It is also able to recognise the goal in terms of type of prepared meal and whether it is healthy. Furthermore, we compare CCBM to state-of-the-art approaches such as Hidden Markov Models (HMM) and decision trees (DT). The results show that our approach performs comparable to the HMM and DT when used for activity recognition. It outperformed the HMM for goal recognition of the type of meal with median accuracy of 1 compared to median accuracy of 0.12 when applying the HMM. Our approach also outperformed the HMM for recognising whether a meal is healthy with a median accuracy of 1 compared to median accuracy of 0.5 with the HMM.
The annotation for the CMU kitchen dataset can be downloaded from http://purl.uni-rostock.de/rosdok/id00000163
Nutrition related health conditions can seriously decrease quality of life; a system able to monitor the kitchen activities and eating behaviour of patients could provide clinicians with important indicators for improving a patient’s condition. To achieve this, the system has to reason about the person’s actions and goals. To address this challenge, we present a behaviour recognition approach that relies on symbolic behaviour repre- sentation and probabilistic reasoning to recognise the person’s actions, the type of meal being prepared and its potential impact on a patient’s health. We test our approach on a cooking dataset containing unscripted kitchen activities recorded with various sensors in a real kitchen. The results show that the approach is able to recognise the sequence of executed actions and the prepared meal, to determine whether it is healthy, and to reason about the possibility of depression based on the type of meal.
Despite the active research into, and the development of, human activity recognition over the decades, existing techniques still have several limitations, in particular, poor performance due to insufficient ground-truth data and little support of intra-class variability of activities (i.e., the same activity may be performed in different ways by different individuals, or even by the same individuals with different time frames). Aiming to tackle these two issues, in this paper, we present a robust activity recognition approach by extracting the intrinsic shared structures from activities to handle intra-class variability, and the approach is embedded into a semi-supervised learning framework by utilizing the learned correlations from both labeled and easily-obtained unlabeled data simultaneously. We use l2,1 minimization on both loss function and regularizations to effectively resist outliers in noisy sensor data and improve recognition accuracy by discerning underlying commonalities from activities. Extensive experimental evaluations on four community-contributed public datasets indicate that with little training samples, our proposed approach outperforms a set of classical supervised learning methods as well as those recently proposed semi-supervised approaches.
We argue that the problem of plan recognition, inferring an agent's plan from observations, is largely a problem of inference under conditions of uncertainty. We present an approach to the plan recognition problem that is based on Bayesian probability theory. In attempting to solve a plan recognition problem we first retrieve candidate explanations. These explanations (sometimes only the most promising ones) are assembled into a plan recognition Bayesian network, which is a representation of a probability distribution over the set of possible explanations. We perform Bayesian updating to choose the most likely interpretation for the set of observed actions. This approach has been implemented in the Wimp3 system for natural language story understanding.
Plan recognition is the problem of inferring the goals and plans of an agent after observing its behavior. Recently, it has been shown that this problem can be solved efficiently, without the need of a plan library, using slightly modified planning algorithms. In this work, we extend this approach to the more general problem of probabilistic plan recognition where a probability distribution over the set of goals is sought under the assumptions that actions have deterministic effects and both agent and observer have complete information about the initial state. We show that this problem can be solved efficiently using classical planners provided that the probability of a partially observed execution given a goal is defined in terms of the cost difference of achieving the goal under two conditions: complying with the observations, and not complying with them. This cost, and hence the posterior goal probabilities, are computed by means of two calls to a classical planner that no longer has to be modified in any way. A number of examples is considered to illustrate the quality, flexibility, and scalability of the approach. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Landmarks for propositional planning tasks are variable assignments that must occur at some point in every solution plan. We propose a novel approach for using landmarks in planning by deriving a pseudo-heuristic and combining it with other heuristics in a search framework. The incorporation of landmark information is shown to improve success rates and solution qualities of a heuristic planner. We furthermore show how additional landmarks and orderings can be found using the information present in multi-valued state variable representations of planning tasks. Compared to previously published approaches, our landmark extraction algorithm provides stronger guarantees of correctness for the generated landmark orderings, and our novel use of landmarks during search solves more planning tasks and delivers considerably better solutions. Yes Yes
We present an approach to keyhole plan recognition which uses a Dynamic Belief Network to represent features of the domain that are needed to identify users' plans and goals. The structure of this network was determined from analysis of the domain. The conditional probability distributions are learned during a training phase, which dynamically builds these probabilities from observations of user behaviour. This approach allows the use of incomplete, sparse and noisy data both during training and testing. We present experimental results of the application of our system to a Multi-User Dungeon adventure game with thousands of possible actions and positions. These results show a high degree of predictive accuracy and indicate that this approach will work in other domains with similar features. Keywords: Plan recognition, Bayesian networks, dynamic belief networks, large domains. 1 Introduction To date, research in plan recognition has focused on three main areas: (1) inferring plans du...
Plan recognition is the problem of inferring the goals and plans of an agent after observing its behavior. Recently, it has been shown that this problem can be solved efficiently, without the need of a plan library, using slightly modified planning algorithms. In this work, we extend this approach to the more general problem of probabilistic plan recognition where a probability distribution over the set of goals is sought under the assumptions that actions have deterministic effects and both agent and observer have complete information about the initial state. We show that this problem can be solved efficiently using classical planners provided that the probability of a partially observed execution given a goal is defined in terms of the cost difference of achieving the goal under two conditions: complying with the observations, and not complying with them. This cost, and hence the posterior goal probabilities, are computed by means of two calls to a classical planner that no longer has to be modified in any way. A number of examples is considered to illustrate the quality, flexibility, and scalability of the approach.
Recognizing goals and plans from complete or partial observations can be efficiently achieved through automated planning techniques. In many applications, it is important to recognize goals and plans not only accurately, but also quickly. To address this challenge, we develop novel goal recognition approaches based on planning techniques that rely on planning landmarks. In automated planning, landmarks are properties (or actions) that cannot be avoided to achieve a goal. We show the applicability of a number of planning techniques with an emphasis on landmarks for goal recognition tasks in two settings: (1) we use the concept of landmarks to develop goal recognition heuristics; and (2) we develop a landmark-based filtering method to refine existing planning-based goal and plan recognition approaches. These recognition approaches are empirically evaluated in experiments over several classical planning domains. We show that our goal recognition approaches yield not only accuracy comparable to (and often higher than) other state-of-the-art techniques, but also result in substantially faster recognition time over existing techniques.
Planning with numeric state variables has been a challenge for many years,
and was a part of the 3rd International Planning Competition (IPC-3). Currently
one of the most popular and successful algorithmic techniques in STRIPS
planning is to guide search by a heuristic function, where the heuristic is
based on relaxing the planning task by ignoring the delete lists of the
available actions. We present a natural extension of ``ignoring delete lists''
to numeric state variables, preserving the relevant theoretical properties of
the STRIPS relaxation under the condition that the numeric task at hand is
``monotonic''. We then identify a subset of the numeric IPC-3 competition
language, ``linear tasks'', where monotonicity can be achieved by
pre-processing. Based on that, we extend the algorithms used in the heuristic
planning system FF to linear tasks. The resulting system Metric-FF is,
according to the IPC-3 results which we discuss, one of the two currently most
efficient numeric planners.
Various Bayesian network classifier learning algorithms are implemented in Weka [10].
Carnegie mellon university multimodal activity (cmu-mmac) database
- F De La Torre
- J Hodgins
- J Montano
- S Valcarcel
- R Forcada
- J Macey
Player goal recognition in open-world digital games with long short-term memory networks
- W Min
- B W Mott
- J P Rowe
- B Liu
- J C Lester
Player goal recognition in open-world digital games with long short-term memory networks
- min