Plan recognition algorithms infer agents' plans from their observed actions. Due to imperfect knowledge about the agent's behavior and the environment, it is often the case that there are multiple hypotheses about an agent's plans that are consistent with the observations, though only one of these hypotheses is correct. This paper addresses the problem of how to disambiguate between hypotheses, by querying the acting agent about whether a candidate plan in one of the hypotheses matches its intentions. This process is performed sequentially and used to update the set of possible hypotheses during the recognition process. The paper defines the sequential plan recognition process (SPRP), which seeks to reduce the number of hypotheses using a minimal number of queries. We propose a number of policies for the SPRP which use maximum likelihood and information gain to choose which plan to query. We show this approach works well in practice on two domains from the literature, significantly reducing the number of hypotheses using fewer queries than a baseline approach. Our results can inform the design of future plan recognition systems that interleave the recognition process with intelligent interventions of their users.
Lastly, the deployment of probes, tests, and sensors to identify the correct diagnoses or the occurrence of events was inspired by work in sequential diagnosis @cite_8 @cite_0 , active diagnosis @cite_3 @cite_12 , and sensor minimization @cite_9 @cite_11 . @cite_2 suggested a metric that will allow an agent to recognize plans earlier. Background Before defining the SPRP we present some background about plans and PR. There are multiple ways to define a plan and the PR problem [inter alia] Nau:2007tf,ramirez2010probabilistic . We follow the definitions used by (simplified for brevity) in which the observing agent is given a plan library describing the expected behaviors of the observed agent. The refinement methods represent how complex actions can be decomposed into (basic or complex) actions. A plan for achieving a complex action @math is a tree whose root is labeled by @math , and each parent node is labeled with a complex action such that its children nodes are a decomposition of its complex action into constituent actions according to one of the refinement methods. The ordering constraints of each refinement method are used to enforce the order in which the method's constituents were executed @cite_19 .
@cite_8 @cite_9 @cite_3 @cite_0 @cite_19 @cite_2 @cite_12 @cite_11
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New healthcare technologies are emerging with the increasing age of the society, where the development of smart homes for monitoring the elders’ activities is in the center of them. Identifying the resident’s activities in an apartment is an important module in such systems. Dense sensing approach aims to embed sensors in the environment to report the detected events continuously. The events are segmented and analyzed via classifiers to identify the corresponding activity. Although several methods were introduced in recent years for detecting simple activities, the recognition of complex ones requires more effort. Due to the different time duration and event density of each activity, finding the best size of the segments is one of the challenges in detecting the activity. Also, using appropriate classifiers that are capable of detecting simple and interleaved activities is the other issue. In this paper, we devised a two-phase approach called CARER (Complex Activity Recognition using Emerging patterns and Random forest). In the first phase, the emerging patterns are mined, and various features of the activities are extracted to build a model using the Random Forest technique. In the second phase, the sequences of events are segmented dynamically by considering their recency and sensor correlation. Then, the segments are analyzed by the generated model from the previous phase to recognize both simple and complex activities. We examined the performance of the devised approach using the CASAS dataset. To do this, first we investigated several classifiers. The outcome showed that the combination of emerging patterns and the random forest provide a higher degree of accuracy. Then, we compared CARER with the static window approach, which used Hidden Markov Model. To have a fair comparison, we replaced the dynamic segmentation module of CARER with the static one. The results showed more than 12 improvement in f-measure. Finally, we compared our work with Dynamic sensor segmentation for real-time activity recognition, which used dynamic segmentation. The f-measure metric demonstrated up to 12.73 improvement. Learning, planning, and representing knowledge at multiple levels of temporal ab- straction are key, longstanding challenges for AI. In this paper we consider how these challenges can be addressed within the mathematical framework of reinforce- ment learning and Markov decision processes (MDPs). We extend the usual notion of action in this framework to include options—closed-loop policies for taking ac- tion over a period of time. Examples of options include picking up an object, going to lunch, and traveling to a distant city, as well as primitive actions such as mus- cle twitches and joint torques. Overall, we show that options enable temporally abstract knowledge and action to be included in the reinforcement learning frame- work in a natural and general way. In particular, we show that options may be used interchangeably with primitive actions in planning methods such as dynamic pro- gramming and in learning methods such as Q-learning. Formally, a set of options defined over an MDP constitutes a semi-Markov decision process (SMDP), and the theory of SMDPs provides the foundation for the theory of options. However, the most interesting issues concern the interplay between the underlying MDP and the SMDP and are thus beyond SMDP theory. We present results for three such cases: 1) we show that the results of planning with options can be used during execution to interrupt options and thereby perform even better than planned, 2) we introduce new intra-option methods that are able to learn about an option from fragments of its execution, and 3) we propose a notion of subgoal that can be used to improve the options themselves. All of these results have precursors in the existing literature; the contribution of this paper is to establish them in a simpler and more general setting with fewer changes to the existing reinforcement learning framework. In particular, we show that these results can be obtained without committing to (or ruling out) any particular approach to state abstraction, hierarchy, function approximation, or the macro-utility problem. The focus of the action understanding literature has predominately been classification, how- ever, there are many applications demanding richer action understanding such as mobile robotics and video search, with solutions to classification, localization and detection. In this paper, we propose a compositional model that leverages a new mid-level representation called compositional trajectories and a locally articulated spatiotemporal deformable parts model (LALSDPM) for fully action understanding. Our methods is advantageous in capturing the variable structure of dynamic human activity over a long range. First, the compositional trajectories capture long-ranging, frequently co-occurring groups of trajectories in space time and represent them in discriminative hierarchies, where human motion is largely separated from camera motion; second, LASTDPM learns a structured model with multi-layer deformable parts to capture multiple levels of articulated motion. We implement our methods and demonstrate state of the art performance on all three problems: action detection, localization, and recognition. Research in discourse analysis, story understanding, and user modeling for expert systems has shown great interest in plan recognition problems. In a plan recognition problem, one is given a fragmented description of actions performed by one or more agents, and expected to infer the overall plan or scenario which explains those actions. This thesis develops the first formal description of the plan recognition process. Beginning with a reified logic of events, the thesis presents a scheme for hierarchically structuring a library of event types. A semantic basis for non-deductive inference, called "minimum covering entailment", justifies the conclusions that one may draw from a set of observed actions. Minimum covering entailment is defined by delineating the class of models in which the library is complete and the set of unrelated observations is minimized. An equivalent proof theory forms a preliminary basis for mechanizing the theory. Equivalence theorems between the proof and model theories are presented. Minimum covering entailment is related to a formalism for non-monotonic inference known as "circumscription". Finally, the thesis describes a number of algorithms which correctly implement the theory, together with a discussion of their complexity. The theory is applied to a number of examples of plan recognition, in domains ranging from an operating system advisor to the theory of speech acts. The thesis shows how problems of medical diagnosis, a similar kind of non-deductive reasoning, can be cast in the framework, and an example previously solved by a medical expert system is worked out in detail. The analyses provides a firm theoretical foundation for much of what is loosely called "frame based inference", and directly accounts for problems of ambiguity, abstraction, and complex temporal interactions, which were ignored by previous work. The framework can be extended to handle difficult phenomena such as errors, and can also be restricted in order to improve its computational properties in specialized domains.