AUTOMATED META-ACTIONS DISCOVERY FOR PERSONALIZED MEDICAL TREATMENTS

Healthcare, among other domains, provides an attractive ground of work for knowl- edge discovery researchers. There exist several branches of health informatics and health data-mining from which we find actionable knowledge discovery is underserved. Actionable knowledge is best represented by patterns of structured actions that in- form decision makers about actions to take rather than providing static information that may or may not hint to actions. The Action rules model is a good example of active structured action patterns that informs us about the actions to perform to reach a desired outcome. It is augmented by the meta-actions model that rep- resents passive structured effects triggered by the application of an action. In this dissertation, we focus primarily on the meta-actions model that can be mapped to medical treatments and their effects in the healthcare arena. Our core contribution lies in structuring meta-actions and their effects (positive, neutral, negative, and side effects) along with mining techniques and evaluation metrics for meta-action effects. In addition to the mining techniques for treatment effects, this dissertation provides analysis and prediction of side effects, personalized action rules, alternatives for treat- ments with negative outcomes, evaluation for treatments success, and personalized recommendations for treatments. We used the tinnitus handicap dataset and the Healthcare Cost and Utilization Project (HCUP) Florida State Inpatient Databases (SID 2010) to validate our work. The results show the efficiency of our methods

Using fuzzy logic to integrate neural networks and knowledge-based systems

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems

Towards Interaction-level Video Action Understanding

A huge amount of videos have been created, spread, and viewed daily. Among these massive videos, the actions and activities of humans account for a large part. We desire machines to understand human actions in videos as this is essential to various applications, including but not limited to autonomous driving cars, security systems, human-robot interactions and healthcare. Towards real intelligent system that is able to interact with humans, video understanding must go beyond simply answering ``what is the action in the video", but be more aware of what those actions mean to humans and be more in line with human thinking, which we call interactive-level action understanding. This thesis identifies three main challenges to approaching interactive-level video action understanding: 1) understanding actions given human consensus; 2) understanding actions based on specific human rules; 3) directly understanding actions in videos via human natural language. For the first challenge, we select video summary as a representative task that aims to select informative frames to retain high-level information based on human annotators' experience. Through self-attention architecture and meta-learning, which jointly process dual representations of visual and sequential information for video summarization, the proposed model is capable of understanding video from human consensus (e.g., how humans think which parts of an action sequence are essential). For the second challenge, our works on action quality assessment utilize transformer decoders to parse the input action into several sub-actions and assess the more fine-grained qualities of the given action, yielding the capability of action understanding given specific human rules. (e.g., how well a diving action performs, how well a robot performs surgery) The third key idea explored in this thesis is to use graph neural networks in an adversarial fashion to understand actions through natural language. We demonstrate the utility of this technique for the video captioning task, which takes an action video as input, outputs natural language, and yields state-of-the-art performance. It can be concluded that the research directions and methods introduced in this thesis provide fundamental components toward interactive-level action understanding

Action patterns for the incremental specification of the execution semantics of visual languages

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. P. Bottoni, J. de Lara, and E. Guerra, "Action Patterns for the Incremental Specification of the Execution Semantics of Visual Languages", IEEE Symposium on Visual Languages and Human-Centric Computing, 2007. VL/HCC 2007,Coeur d'Alene, ID, 2007, pp. 163-170We present a new approach - based on graph transformation - to incremental specification of the operational (execution) semantics of visual languages. The approach combines editing rules with two meta-models: one to define the concrete syntax and one for the static semantics. We introduce the notion of action patterns, defining basic actions (e.g. consuming or producing a token in transition-based semantics), in a way similar to graph transformation rules. The application of action patterns to a static semantics editing rule produces a meta-rule, to be paired with the firing of the corresponding syntactic rule to incrementally build an execution rule. An execution rule is thus tailored to any active element (e.g. a transition in a Petri net model) in the model. Examples from Petri nets, state automata and workflow languages illustrate these ideas.Work sponsored by the EC with contract HPRN-CT-2002-00275, SegraVis, and the Spanish Ministry of Science and Education, projects MD2 (TIC200303654) and MOSAIC (TSI2005-08225-C07-06

Action patterns for the incremental specification of the execution semantics of visual languages

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. P. Bottoni, J. de Lara, and E. Guerra, "Action Patterns for the Incremental Specification of the Execution Semantics of Visual Languages", IEEE Symposium on Visual Languages and Human-Centric Computing, 2007. VL/HCC 2007,Coeur d'Alene, ID, 2007, pp. 163-170We present a new approach - based on graph transformation - to incremental specification of the operational (execution) semantics of visual languages. The approach combines editing rules with two meta-models: one to define the concrete syntax and one for the static semantics. We introduce the notion of action patterns, defining basic actions (e.g. consuming or producing a token in transition-based semantics), in a way similar to graph transformation rules. The application of action patterns to a static semantics editing rule produces a meta-rule, to be paired with the firing of the corresponding syntactic rule to incrementally build an execution rule. An execution rule is thus tailored to any active element (e.g. a transition in a Petri net model) in the model. Examples from Petri nets, state automata and workflow languages illustrate these ideas.Work sponsored by the EC with contract HPRN-CT-2002-00275, SegraVis, and the Spanish Ministry of Science and Education, projects MD2 (TIC200303654) and MOSAIC (TSI2005-08225-C07-06

FIPA Communicative Acts in Defeasible Logic

In agent communication languages, the inferences that can be made on the basis of a communicative action are inherently conditional, and non-monotonic. For example, a proposal only leads to a commitment, on the condition that it is accepted. And in a persuasion dialogue, assertions may later be retracted. In this paper we therefore present a defeasible logic that can be used to express a semantics for agent communication languages, and to efficiently make inferences on the basis of communicative actions. The logic is non-monotonic, allows nested rules and mental attitudes as the content of communicative actions, and has an explicit way of expressing persistence over time. Moreover, it expresses that mental attitudes are publicly attributed to agents playing roles in the dialogue. To illustrate the usefulness of the logic, we reformalize the meta-theory underlying the FIPA semantics for agent communication, focusing on inform and propose. We show how composed speech acts can be formalized, and extend the semantics with an account of persuasion

Optimization of swarm robotic constellation communication for object detection and event recognition

Swarm robotics research describes the study of how a group of relatively simple physically embodied agents can, through their interaction collectively accomplish tasks which are far beyond the capabilities of a single agent. This self organizing but decentralized form of intelligence requires that all members are autonomous and act upon their available information. From this information they are able to decide their behavior and take the appropriate action. A global behavior can then be witnessed that is derived from the local behaviors of each agent. The presented research introduces the novel method for optimizing the communication and the processing of communicated data for the purpose of detecting large scale meta object or event, denoted as meta event, which are unquantifiable through a single robotic agent. The ability of a swarm of robotic agents to cover a relatively large physical environment and their ability to detect changes or anomalies within the environment is especially advantageous for the detection of objects and the recognition of events such as oil spills, hurricanes, and large scale security monitoring. In contrast a single robot, even with much greater capabilities, could not explore or cover multiple areas of the same environment simultaneously. Many previous swarm behaviors have been developed focusing on the rules governing the local agent to agent behaviors of separation, alignment, and cohesion. By effectively optimizing these simple behaviors in coordination, through cooperative and competitive actions based on a chosen local behavior, it is possible to achieve an optimized global emergent behavior of locating a meta object or event. From the local to global relationship an optimized control algorithm was developed following the basic rules of swarm behavior for the purpose of meta event detection and recognition. Results of this optimized control algorithm are presented and compared with other work in the field of swarm robotics

The agent programming language meta-APL

Abstract. We describe a novel agent programming language, Meta-APL, and give its operational semantics. Meta-APL allows both agent programs and their associated deliberation strategy to be encoded in the same programming language. We define a notion of equivalence between programs written in different agent programming languages based on the notion of weak bisimulation equivalence. We show how to simulate (up to this notion of equivalence) programs written in other agent programming languages by programs of Meta-APL. This involves translating both the agent program and the deliberation strategy under which it is executed into Meta-APL.

A Rigorous Approach to Relate Enterprise and Computational Viewpoints

Multiviewpoint approaches allow stakeholders to design a system from stakeholder-specific viewpoints. By this, a separation of concerns is achieved, which makes designs more manageable. However, to construct a consistent multiviewpoint design, the relations between viewpoints must be defined precisely, so that the consistency of designs from these viewpoints can be verified. The goal of this paper is to make the consistency rules between (a slightly adapted version of) the RM-ODP enterprise and computational viewpoints more precise and to make checking the consistency between these viewpoints practically applicable. To achieve this goal, we apply a generic framework for relating viewpoints that includes reusable consistency rules. We implemented the consistency rules in a tool to show their applicability

Applying the business process and practice alignment meta-model: Daily practices and process modelling

Background: Business Process Modelling (BPM) is one of the most important phases of information system design. Business Process (BP) meta-models allow capturing informational and behavioural aspects of business processes. Unfortunately, standard BP meta-modelling approaches focus just on process description, providing different BP models. It is not possible to compare and identify related daily practices in order to improve BP models. This lack of information implies that further research in BP meta-models is needed to reflect the evolution/change in BP. Considering this limitation, this paper introduces a new BP meta-model designed by Business Process and Practice Alignment Meta-model (BPPAMeta-model). Our intention is to present a meta-model that addresses features related to the alignment between daily work practices and BP descriptions. Objectives: This paper intends to present a meta-model which is going to integrate daily work information into coherent and sound process definitions. Methods/Approach: The methodology employed in the research follows a design-science approach. Results: The results of the case study are related to the application of the proposed meta-model to align the specification of a BP model with work practices models. Conclusions: This meta-model can be used within the BPPAM methodology to specify or improve business processes models based on work practice descriptions