Emotion and Adaptation

Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.

Artificial intelligence in medicine

Simulating the motion of realistic, large, dense crowds of autonomous agents is still a challenge for the computer graphics community. Typical approaches either resemble particle simulations (where agents lack orientation controls) or are conservative in the range of human motion possible (agents lack psychological state and aren't allowed to 'push' each other). Our HiDAC system (for High-Density Autonomous Crowds) focuses on the problem of simulating the local motion and global wayfinding behaviors of crowds moving in a natural manner within dynamically changing virtual environments. By applying a combination of psychological and geometrical rules with a social and physical forces model, HiDAC exhibits a wide variety of emergent behaviors from agent line formation to pushing behavior and its consequences; relative to the current situation, personalities of the individuals and perceived social density.

/pdf/controlling-individual-agents-in-high-density-crowd-1zkmq0407w.pdf

Controlling individual agents in high-density crowd simulation

Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation

A solution to get the problem off, have you found it? Really? What kind of solution do you resolve the problem? From what sources? Well, there are so many questions that we utter every day. No matter how you will get the solution, it will mean better. You can take the reference from some books. And the redefining health care creating value based competition on results is one book that we really recommend you to read, to get more solutions in solving this problem.

Redefining Health Care: Creating Value-Based Competition on Results

Many producers and consumers of legacy training simulator and game environments are beginning to envision a new era where psycho-socio-physiologic models could be interoperated to enhance their environments' simulation of human agents This paper explores whether we could embed our behavior modeling framework (described in the companion paper, Part I) behind a legacy first person shooter 3D game environment to recreate portions of the Black Hawk Down scenario Section I amplifies the interoperability needs and challenges confronting the field, presents the questions that are examined, and describes the test scenario Sections 2 and 3 review the software and knowledge engineering methodology, respectively, needed to create the system and populate it with bots Results (Section 4) and discussion (Section 5) reveal that we were able to generate plausible and adaptive recreations of Somalian crowds, militia, women acting as shields, suicide bombers, and more Also, there are specific lessons learned about ways to advance the field so that such interoperabilities will become more affordable and widespread

/pdf/human-behavior-models-for-agents-in-simulators-and-games-3ds9sjacoo.pdf

Human behavior models for agents in simulators and games: part II: gamebot engineering with PMFserv

https://repository.upenn.edu/cgi/viewcontent.cgi?article=1768&context=ese_papers

A systems approach to healthcare

Validating social systems is not a trivial task. The paper outlines some of our past efforts in validating models of social systems with cognitively detailed agents. It also presents some of the challenges faced by us. A social system built primarily of cognitively detailed agents can provide multiple levels of correspondence, both at observable and abstract aggregated levels. Such a system can also pose several challenges including large feature spaces, issues in information elicitation with database, experts and news feeds, counterfactuals, fragmented theoretical base, and limited funding for validation. Our own approach to validity assessment is to consider the entire life cycle and assess the validity under four broad dimensions of methodological validity, internal validity, external validity and qualitative, causal and narrative validity. In the past, we have employed a triangulation of multiple validation techniques, including face validation as well as formal validation tests including correspondence testing.

/pdf/validating-agent-based-social-systems-models-2o9n4u02kk.pdf

Validating agent based social systems models

This paper presents a theory for role-playing simulation games intended to support analysts (and trainees) with generating and testing alternative competing hypotheses on how to influence world conflict situations. Simulated leaders and followers capable of playing these games are implemented in a cognitive modeling framework, called the Performance Moderator Function Server (PMFserv), which covers value systems, personality and cultural factors, emotions, relationships, perception, stress/coping style, and decision making. Of direct interest, as Section I-A explains, is codification and synthesis of best-of-breed social-science models within PMFserv to improve the internal validity of agent implementations. Sections II and III present this for leader profiling instruments and group-membership decision making, respectively. Section IV then offers two real-world case studies (The Third Crusade and SE Asia Today) where agent models are subjected to Turing and correspondence tests under each case study. In sum, substantial effort on game realism, best-of-breed social-science models, and agent validation efforts is essential if analysis and training tools are to help explore cultural issues and alternative ways to influence outcomes. Such exercises, in turn, are likely to improve the state of the science as well.

/pdf/sociocultural-games-for-training-and-analysis-xn2i1wkncd.pdf

Gnana Bharathy

Papers

Human behavior models for agents in simulators and games: part II: gamebot engineering with PMFserv

A systems approach to healthcare

Validating agent based social systems models

Sociocultural Games for Training and Analysis

A systematic development and validation approach to a novel agent-based modeling of occupant behaviors in commercial buildings