Computational cognitive assistants for futures studies: Toward vision based simulation

Accepted Manuscript Title: Computational Cognitive Assistants for Futures Studies: Toward Vision Based Simulation Author: Meisam Ahmadi Mohammadreza Jahed Motlagh Adel Torkaman Rahmani Mohammad Mahdi Zolfagharzadeh Peyman Shariatpanahi PII: DOI: Reference:

S0016-3287(16)30082-9 http://dx.doi.org/doi:10.1016/j.futures.2016.03.010 JFTR 2110

To appear in: Received date: Revised date: Accepted date:

30-6-2015 14-3-2016 16-3-2016

Please cite this article as: Meisam Ahmadi, Mohammadreza Jahed Motlagh, Adel Torkaman Rahmani, Mohammad Mahdi Zolfagharzadeh, Peyman Shariatpanahi, Computational Cognitive Assistants for Futures Studies: Toward Vision Based Simulation, (2016), http://dx.doi.org/10.1016/j.futures.2016.03.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

*Title Page with Author Information

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Computational Cognitive Assistants for Futures Studies: Toward Vision Based Simulation Meisam Ahmadia , Mohammadreza Jahed Motlagha,∗, Adel Torkaman Rahmania , Mohammad Mahdi Zolfagharzadehb , Peyman Shariatpanahic a Computer

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Engineering Department, Iran University Of Science and Technology, Tehran, I.R Iran b Faculty of Management, University of Tehran, Tehran, I.R Iran c Institute of Biochemistry and Biophysics, University of Tehran, Tehran , Iran

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Abstract

Many foresight researchers believe that quantitative simulations have a very

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restricted contribution in futures studies due to their simplicity and lack of creativity. While qualitative methods, taking advantage of the human cognitive system, have a great potential in addressing a wide range of problems in futures

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studies, this potential is mostly due to the human visual logic that can handle the task of imagining future scenarios much better than mathematical logic.

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On the other hand, computational methods benefit from the advantages of silicon-based systems namely speed, large memory, rapid networking, and

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communication. Hence, it would be extremely beneficial to come up with a solution that combines the positive sides of both qualitative and computational approaches. Cognitive artificial agents are computational units that make use of the human cognitive system. Their interaction with foresight and futures researchers can result in promising solutions for the problems addressed in futures studies. In addition, these agents can serve as a great source of inspiration for taking the first step towards vision based computers that can simulate humans’ imaginations of the future.

This paper reviews some of the previous attempts in this field and finally sheds light on the main issues where methods in futures studies can play a ∗ Corresponding

author Email address: [email protected] (Mohammadreza Jahed Motlagh)

Preprint submitted to Fututres

March 14, 2016

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key role in the future of Human Computer Interaction systems. Our suggested architecture for a future studies interactions-based system along with its justi-

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fications and specifications is provided in the form of a request for proposal. Keywords: Futures Studies, Quantitative and Qualitative Methods, HCI

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Design, Cognitive Architecture, Artificial Intelligent Agents.

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*Manuscript WITHOUT Author Identifiers Click here to view linked References

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Computational Cognitive Assistants for Futures Studies: Toward Vision Based Simulation

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Abstract

Many foresight researchers believe that quantitative simulations have a very restricted contribution in futures studies due to their simplicity and lack of

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creativity. While qualitative methods, taking advantage of the human cognitive system, have a great potential in addressing a wide range of problems in futures studies, this potential is mostly due to the human visual logic that can handle

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the task of imagining future scenarios much better than mathematical logic. On the other hand, computational methods benefit from the advantages of silicon-based systems namely speed, large memory, rapid networking, and

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communication. Hence, it would be extremely beneficial to come up with a

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solution that combines the positive sides of both qualitative and computational approaches. Cognitive artificial agents are computational units that make use of the human cognitive system. Their interaction with foresight and futures re-

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searchers can result in promising solutions for the problems addressed in futures studies. In addition, these agents can serve as a great source of inspiration for taking the first step towards vision based computers that can simulate humans’ imaginations of the future.

This paper reviews some of the previous attempts in this field and finally

sheds light on the main issues where methods in futures studies can play a key role in the future of Human Computer Interaction systems. Our suggested architecture for a future studies interactions-based system along with its justifications and specifications is provided in the form of a request for proposal. Keywords: Futures Studies, Quantitative and Qualitative Methods, HCI Design, Cognitive Architecture, Artificial Intelligent Agents.

Preprint submitted to Fututres

March 14, 2016

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1. Introduction

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Futures studies were born in the context of an empirical tradition where quantitative methods had a prominent position[1][2]. Predictive quantitative

methods such as system dynamics played an important role in the field during

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the 1970s. The Limits of Growth is one of the major works of the field from that era [3]. However gradually due to the complexity of futures studies problems,

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foresight and futures researchers turned their focus to qualitative methods[4][5], where they could use their imaginations to examine various complex scenarios for the future.

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This shift took place mostly as a result of the incredible human capacity of being able to visually imagine a wide range of possible futures and to have a

futures and evaluate them.

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visual cognition and reasoning system that permits him/her to go through these

In the beginning, the goal of futures researchers was to predict a uniquely

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predicted future, but over time the goal of predicting a unique future was replaced by the prediction of a set of possible and probable outcomes.

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Popper [2] has outlined five complementary phases for a foresight process: 1Pre-foresight, 2- Recruitment, 3- Generation, 4- Action and 5- Renewal. While human visual cognition and imagination are commonly used in the Generation

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and Action phases, other phases can mostly be handled mechanically. Methods such as Wild cards, Scenario planning, Expert panels and Causal

Layered Analysis have found many applications in futures studies and specifically in the generation and action phases. In short, these methods can help one’s imagination to systematically consider different possible scenarios for the future. This imagination originates from the individual’s cognition which is composed of various elements such as memory, imagination, behavior, creativity, etc.[6]. In addition to these qualitative methods there is an enormous amount of effort being put in the field of quantitative modeling and simulation methods in order to overcome the complexity of the problem of future[7] and to contribute to solving problems mostly in the generation and action phases. Some of the

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most promising approaches among these are mentioned below:

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• Building complex frameworks to deal with unexpectedness: Methods such as agent-based modeling, neural networks and cellular automata are instances of methods that adopt this approach. For example, these meth-

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ods are used for predicting the trajectory of the market share of new

technologies[8][9], tackling future-oriented issues involving creation and

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diffusion of innovations [10][11][12][13][14], analyzing real-world scenarios[15], simulating urban future scenarios[12][14], and developing forecasting mod-

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els for trend analysis[16].

• System Dynamics models that take into account feedbacks, system memory , and nonlinear effects: These models are used for instance in areas

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such as sustainable development and long-term perspectives on future resource limitations [17][18][19][20][21], modeling urban expansion scenarios [14][22][23], market forecasting[24], and scenario testing in the seven

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core areas (demographics, economics, agriculture, energy, environment,

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technology, and socio-political factors) in the International Futures model which benefits from a combination of system dynamics, econometrics and agent classes modeling[25].

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• Mimicking the processes observed in the complex world of biology in order to overcome the complexities in social sciences and economics: Notable examples of such approaches include Evolutionary dynamics, game theory, and evolutionary game theory. Instances of application of these approaches include investigation of cooperation dynamics such as strategic alliance[26], co-evolutionary dynamics between firms[27], environmental problems[28][29], evolution of cooperations in the public goods game[30] , and forecasting actors’ decisions in socio-economic systems[31]. • Models aimed at finding conditions in which a system shows chaotic behavior: Some examples are economic systems like market forecasting [32][33], behavior prediction of social systems such as education, public opinion 3

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and politics[34][35][36] and new technology diffusion modeling [37][34].

technological forecasting but as Bishop and Hines(2012) stated[4]:

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These methods have been successful in certain areas such as economic and

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Quantitative forecasting has become less used by futurists over time as it became clear that the world is simply too complex to simply

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plug formulas into models and get the right answer. Nonetheless, systems thinking and newer related ideas, such as chaos and complexity, have developed along with simulations and gaming and

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remain important tools in the futurist tool kit.

The main reason that these quantitative methods are not able to compete with their qualitative counterparts is the lack of a vision-based cognitive and

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logical component. A novel approach to the problem is to make simulation methods that harness the cognitive advantages of the human mind. This means

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finding ways for altering the hard and algorithmic reasoning systems of current computers to make them more similar to the soft, visualized, and qualitative

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reasoning system of the human mind. Such a computational system can then help foresight and futures researchers in the form of an intelligent assistant that can go beyond the boundaries of the researcher’s creativity and imagination

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rather than restricting his/her thoughts and ideas within the limits of a rigid computational framework.

Imagine an assistant with the same reasoning logic as us, but with the ability

to consider multiple different futures at a much faster rate and qualitatively judge their plausibility. This genius assistant has a memory as large as the whole of the Internet with access to unimaginably vast resources of products of people’s creativity such as films, stories, photos, etc. Enjoying the aid of such an assistant can greatly expand the limits of an individual’s imagination and creativity. The assistant is able to go through all of this memory in a flash of time and choose items that will be most helpful. This assistant learns the logic in already designed qualitative methods such as scenario building, CLA, and

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brainstorming and searches over a much bigger expanse than what any human can possibly build scenarios for.

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In the following, we extract the characteristics of such an assistant and its interaction environment with the user based on the nature of knowledge in

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foresight. Then, based on the specifications we propose, different features of

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this system will be explored.

2. Futures Studies Workspace

Popper defines Futures Studies as a common space for open thinking that

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accelerates strategic approaches[2]. Different methods have been introduced to build this space. These methods can be broken down into five categories based

3- Interaction, 4-Creativity.

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on their capability to gather or process information: 1- Evidence, 2-Expertise,

Figure 1 shows a version of Popper’s diamond illustrating some of the famous

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computational modeling methods mostly used in the Generation stage of the foresight process [2]. As it can be seen, all of these modeling methods are found

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at the bottom of the diamond near the Evidence corner. The question is why these types of computational methods do not play a significant role in other areas.

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Figure 1: Computer Methods In Popper’s Diamond

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In short, this can be viewed in the light of the nature of these methods and comparing them with the upper qualitative generative methods: These modeling

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techniques are based on ”digit based computation” while more creative methods take advantage of ”human vision based computation”.

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The human creativity, as it is referred to in Popper’s diamond, in fact corre-

sponds to the well-known concept that is referred to in psychology as imagina-

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tion, and is based on vision computation [38]. That is exactly why visualization is rapidly finding new different applications in qualitative futures studies[39]. But is there any way to make use of the enormous potential of machines in

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these areas?

There seem to be at least two possibilities: 1) Inventing vision based computers, 2) Having a well-defined interaction between Human and Machine which

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uses the advantages of both. The first option, vision based machines, is a relatively new branch in computer science and is still far from finding real applications in Futures Studies.

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As for the second option, i.e. well-defined human-machine interaction, one

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approach is to use the aid of smart assistants that can help foresight researchers through boosting creativity, level of interaction, expertise and even access to evidence. Such an assistant can also be accounted as the first step toward the

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”Vision-based computer”. Human-Computer Interaction is the specific field focusing on this relationship’s characteristics and specifications. In this paper we present some of the potential implications of having a powerful foresight researcher assistant, which specifically helps the researcher in boosting imagination and creativity, focusing on the human-computer interaction mechanisms that govern the interactions between the assistant and the researcher. In addition, we propose a Human-Computer Interaction architecture that is the first step towards creating such assistants. 2.1. An assistant for creativity As discussed above most of the Futures Studies methods used in the Generation stage of foresight are qualitative and based on creativity. 6

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As Heinonen indicates [39] imagination plays a key role in creative futures studies. In order for creativity to come about, an imagination of the subject

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matter should first be formed. Afterwards this imagination can undergo innovative changes, which is what we refer to creativity. Thus, boosting a foresight

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researchers creative capacity amounts to helping him/her to have an innovative imagination.

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The solution consists of a creative workspace, for futures imagination, and an artificially intelligent assistant helping foresight researchers to use different tools in the workspace. The user acts in a virtual workspace and the assistant

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perceives his/her desire in order to help in various ways( Figure 2 ). Different Future Studies issues, for examples different scenarios or expert outcomes, are gathered in the workspace while the assistant helps the foresight researcher to

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use them to make a visual futures window [39]. For instance, the assistant, using different artificial intelligence techniques, can search through the Internet to find suitable visions (e.g., scenarios, photos, videos, etc..) to help in solving

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happen in the future is.

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the puzzle of what the visual representation of the scenario that will actually

In addition to creativity, this workspace is able to help foresight researchers with the factors in the other corners of Poppers’ diamond. For example through

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access to the huge sources of data available on the world wide web and using statistical methods, the assistant can help experts in data gathering and data visualization. Being familiar with the human visual logic, it will be able to convert the text and numerical data to images, graphs, patterns and animations for better understanding. In addition, using data mining and pattern recognition techniques it is able to produce knowledge with high levels of abstraction out of big data.

Accounting for the expertise and interaction capabilities, this workspace can help experts benefit from vision based interactions like visionary Delphi[40] and Experts panels. This type of communication among experts can raise the level of interaction from text to vision, resulting in a much higher level of mutual understanding and convergence[40]. Getting closer to human cognition, this 7

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Figure 2: Researcher - Workspace - Assistant

workspace is able to perceive, understand, compare ideas and even build a new

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ones by combining the ideas of different experts. In other words, learning from human experts, this intelligent agent acts like an expert itself and more impor-

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tantly it can interact with other intelligent agents through accurate, fast and effective means of interaction. This community of artificial experts( Figure 3)

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may even be able to produce creative ideas by itself.

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Figure 3: Community Of Assistants

2.2. Specifications of the Human Computer Interaction system The main parts of a Human-Computer Interaction system are the software and hardware with which the human user works. Meeting certain criteria is necessary for a Human-Computer Interaction system in order for it to be able

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to support the proposal presented above for the future studies assistant. These criteria will be introduced later in this article as part of the specifications for

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the desired Human-Computer Interaction system. 2.2.1. Smart Environment

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In spite of the vastness and complexity of the data involved and the numerous

methods developed, the environment of interaction should be such that the

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user needs a very low level of specialization for being able to use it. This is especially crucial for futures researchers who are not necessarily specialists in

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the problem content but are rather helping experts achieve a suitable vision. The very nature of the system must be readily understandable and the dynamics of the interaction must be as close as possible to human assumptions. Based on

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this, the environment should be able to support two fundamental features: 1- Smart input: In order to have a better understanding of the user, the environment must be familiar with the user’s outputs, behaviors, knowledge,

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and feelings. 2- Smart output: The environment must be able to present its output in a

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format that is as easily understandable to the user as possible. This would be achievable using vision production methods.

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2.2.2. Knowledge

The environment must be able to easily convert and raise data with different

abstraction levels to higher levels of knowledge. For example, it should be able to convert raw data into patterns, graphs, images, animations and other high abstraction knowledge types. 2.2.3. Communication

Communication is a key feature of the environment. There are two types of communication: 1- Communication with other machines and agent-based systems in order to complete the knowledge and experience repository.

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2- Communication with other experts for building common spaces for creat-

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ing and sharing knowledge. 2.2.4. Believability

Finally, the most important feature that guarantees the success of these

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systems in the future of Human-Computer Interaction is its smart believability;

its intelligence is raised to the level of a real-like amiable assistant. This goal

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3. Review: Human Computer Interaction

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necessitates using novel architectures and standards.

Today, extensive research is being conducted on the current state and future of human-computer interaction systems. Three periods are usually considered

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in the history of human-computer interaction [41]. In the first period, when mainframes were introduced, there were only a few computers for a huge number of humans. In the second period, there was a personal computer for each

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person. And in the third period, also known as the ubiquitous computing pe-

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riod, each human will be in interaction with many computing instruments that are available in his/her environment, and provide service to him/her. Some like Faroogh et al. provided the surface interaction and photonic

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computing-based solutions to increase efficiency over time in keeping pace with the requirements of the future world[42]. Some others have challenged conventional evaluation methods and tried to provide futuristic solutions to evaluate human-computer interaction[43]. Seffah et al. used software engineering methods to produce human-computer interaction, and provided futures studies based on such methods[44]. And finally, there are many works that have studied the future of human-computer interaction in 2020 or the new century[45] [46][47]. The present study tries to study the future of human-computer interaction, and, accordingly, provide suggestions for improving the current condition and for directing the progress towards a favorable future.

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3.1. Human-Computer Interaction Design An appropriate Human-computer interaction system is based on a design

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that is the best fit between the human user, the computer, and the services intended and required in the system[48]. This performance is important in terms

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of quality as well as other service optimality features. In this design the comput-

ers must serve the human, and the interactions must be made between the user

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and the computer to produce the intended service. The design is dependent on many factors, including the user, the interaction environment, specific problem requirements, and the quality expected from the solution. These factors will be

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further discussed in the next sections.

The designer of a human-computer interaction system tries, based on his/her knowledge of the problem and the users requirements on the one hand, and the

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internal and behavioral characteristics of the user on the other hand, to use one of the available interaction design methods, to provide a new design for the interaction. Design methods are frameworks that can be used by the designer

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along with the environment and the specific characteristics of the problem as

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the basis for creating new interactions.

For example, infrastructures like the Internet and the world wide web lead to the creation of environments for interaction between users and systems. In

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such environments interaction methods consisting of a set of instruments are innovated, and the designer uses those instruments according to the specific requirement of his problem. Such methods sometimes go beyond just a number of instruments and suggest a more conceptual view of the interaction itself as well as the two sides of the interaction, i.e., the human and the computer. Such a view is a great help to the designer of the interaction, and also the software engineer and the system developer in enabling them to produce the new system in full details and in full complexity in the form of a premeditated method. Today, the development of adaptive human-computer interaction has presented designers a very different service from the simple services that used to be provided previously[49]. This will become even more noticeable in the future, when due to the increasing growth of ubiquitous computing, humans will 11

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be faced with a huge number of intelligent computing objects that have no difficulty communicating with one another and can easily offer the required

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services[50]. Therefore, we need to examine the future of human-computer interaction technologies, so that we can provide a proper framework for the design

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and production of interaction systems based on the requirements concluded from this examination.

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3.2. Features of Future human-computer interaction Systems

As discussed earlier, as a result of increasing hardware capabilities and per-

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vasion of IT corporations, the world rapidly became abundant with devices that have computing processors and are able to communicate information with the physical world. This increased hardware capacity has provided a good ground

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for software development in the field of intelligent systems. Today, internet of things has become a common topic in the literature, and many of its aspects have already been studied. Additionally, the use of such intelligent devices

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is rapidly increasing in personal life, business, public services, education, and

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many other domains[51][52].

Such increased hardware and software capability has a direct effect on methods of interactions. For example, while in the past data collection mainly relied on form-based methods, today there are many ways to obtain objective and user

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model based data about the users, for example by examining their gestures, or analyzing their inclinations and tendencies via tracking ther eye movements[53]. The recent emergence of pervasive computing and pervasive intelligence pre-

dicted the use of multi-agent and mutually independent systems to shape the overall interaction space[53]. In this view, using intelligent agent design models, each system or part of a system must have an objective, and they must solve different problems based on the knowledge they acquire from their environment and by interacting with another agent. Using such an approach has a direct effect on thinking, and has already significantly affected the way many systems are developed. Researchers working in this area today take a wide range of issues into consideration such as the manner of communication between agents 12

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and the different standards governing these communications, the way the ob-

forming whole problems from different partial interactions.

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jective is pursued, and ways in which it is possible to perform tasks such as

It is crucial to note that the advancement of technology, computing power,

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and intelligent capability must be used to provide better service to human users.

Perhaps in the early years of the formation of human-computer interaction, due

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to the limited functionality of computers and the users desire to use computers for solving as many different problems as possible, it was the human who served rather than the computer when it came to manner of interaction. That is, the

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focus was on problem-solving and in the meantime a human had to help the computer in solving the problem by learning the computers new capabilities. For example, he had to learn how to type, while typing is not a natural and

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inborn human capability, and had to spend a major portion of his day using a computer, mobile device, or tablets, all of which keep him away from natural living environment. Today, in many research centers, the idea of leading

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future interactions towards becoming human-centered has received significant

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attention[54]. Many have considered the many disadvantages of conventional human-computer interaction systems and have predicted a dark future in case the current trend continues. In designing the right scenario for the future, sev-

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eral issues need to be addressed including the necessity of changing the current trend so that the human side becomes the center of the interaction, making the human the side that is supposed to be served rather than the computer, relying on natural human qualities for the interaction, making sure that the interaction serves human development, and finally, seeking ways for promoting these features [47].

Circles of human relationship such as family, friends and neighbors along

with the moral standards that govern these domains have become established over the long history of human life. The presence of these technologies in these environments can have a destructive effect on such old institutions. By ignoring the significance of these institutions, new technologies can create a new atmosphere whose destructive effects are unknown until they lead to a disaster. 13

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The question that should be asked here is whether it is possible to design interaction environments such that it is the computer that is brought to the

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humans natural environment, instead of the computer taking the human away

from the human community. The suggestion here is to produce a servant ma-

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chine with the features of a human servant, or an intellectual research assistant

that not only doesn’t take the human away from his natural life environment,

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but also helps him further develop his previous achievements.

Similarly, one can ask whether it is possible to have intelligent agents with high computing powers and ubiquitous intelligence who are familiar with the

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human nature including the cognitive aspect of the human mind and serve the users objective. These agents are expected to be like living intelligent beings that can understand their audience and show behaviors similar to those of a

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living being in human-friendly environments. Suppose a small community in which each household keeps a small trained dragon as a pet that functions as a friend of their children[55]. In such a scenario although these creatures are

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timid and their food and waste are natural they greatly influence the culture

community.

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and lifestyle of the community, resulting in the formation of an entirely different

This story can be an allegory of the human community today; however, what

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is going on in the human community today is happening in a much larger scale, and will grow even bigger in the future if the current trend continues. Today, several silicon intelligence devices are found in every house, or even with each person, that have nothing in common with the human nature, and have largely altered the human lifestyle. It is therefore necessary to change the current trend, and to redesign these devices as agents acting within a fully human system, so that humans can go back to their natural course of life, and human development is directed in a way that is consistent with its nature. Another noticeable problem with the conventional systems is lack of coordination between software engineering and human-computer interaction in the creation of an interaction system[44]. For example, suppose that an education expert intends to create a software with certain specific goals. The unavoidable 14

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fact is the need for a software engineer to analyze the experts demand. Afterwards a detailed software design is prepared, and finally, a technical team of

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developers produce the software product. This process of software production

is a complex process that usually does not involve learning from or adaptation

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with the user, and as a result, incurs costs of post-production changes that are

tens of times more than the costs of changes that would be made during the

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course of design.

The goal that should be pursued here is therefore to direct future software systems, and generally, future interaction systems, in a way that they provide

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a simple design environment for experts, so that the experts can explain the intended requirements easily, and as a result, a high quality end products can be produced. Moreover, one can ask whether it is possible to use agents that

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can produce automatic user interfaces that take into account the user’s preferences and the environment through learning, instead of using the conventional

and development.

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method that involves the complex and laborious processes of analysis, design,

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In the next sections the requirements of a human-computer interaction design method are discussed with a look at the prospect of technology. First the question of why intelligent agents must have a cognitive nature is discussed and

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afterwards a new framework for human-computer interaction is proposed. 3.3. Cognitive Architectures as Cognitive Intelligent Agents In Interaction And Design

We are one side of the human-computer interaction; we are humans who

are interacting with their environment every day, and who now intend to add computers to these interactions. Since the time when the issue of interaction between humans and computers was first introduced, the natural characteristics of human beings including their physical and psychological features have been a focus of attention of the pioneers of this field[56]. Accordingly, many studies have been carried out on the manner in which these interactions affect humans. In the meantime, specialists in the field of cognitive science have made the 15

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greatest contribution to studies in this field.They have used cognitive science frameworks for developing models of the users behavior to create interaction

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systems based on these models[57]. Various pieces of hardware have been de-

veloped for understanding the manner in which users behave. However, the

cr

industry and the market have produced and supplied many products aimed at gaining a higher profit and ignoring their effects[58]. Even when some of the

us

adverse effects were reported in some cases, they were not considered later in the design of new products[59].

Nonetheless, cognitive science has made progress as the most important

an

field affecting the design of user interaction systems.Many researches have been carried out on the manner in which these interactions affect the user, with significant results[60]. Some studies have used cognitive methods and tried to

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produce interactive agents using the human cognitive model. That is, they have tried to produce cognitive agents that understand the physical world in the same way that humans do, and to simulate cognitive decision-making methods based

d

on the way humans use their reason, understanding, and sensory perceptions of

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the physical world to decide and act. These studies have then tried to provide behavioral models of agents based on cognitive models. Among such works are those that have studied the relationship between sensors and motors, or those

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that use mental models in human-computer interaction. There have also been many attempts in the field of cognitive science to understand emotions and the way in which emotions are expressed[61]. Cognitive architectures are the subject of researches that develop human-

computer interaction systems based on cognitive science. Byrne provided an interesting definition of cognitive architecture[62]. According to him, cognitive architecture is a broad theory of human cognition that is made of a large set of experimental data, and is implemented in a computer simulation. That is, cognitive architectures try to integrate different fields related to human cognition to produce something that shows behaviors similar to those of a human. The crucial question is whether cognitive architectures can be sufficiently successful in the field of human-computer interaction. One side of Human16

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Computer Interactions is always a human. On many occasions, we seek to have a proper model of the user, so that we can use it to obtain a more precise prediction

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of the users behavior in the course of using the system. This is a subject that has been discussed thoroughly in the literature on cognitive architectures and

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human-computer interaction. In some of these works, instead of the user, it is a

cognitive architecture that interacts with the system, and based on the results

of the effects of the interaction on the user.

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of such interactions, a model of user behavior can be created, allowing the study

In another part of these studies, cognitive architectures come to help com-

an

puter systems at runtime during interaction, helping them to obtain a proper model of the user by observing his/her behavior. This model plays an essential role in ensuring quality performance of software products in many computer

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systems.

However, in addition to the production of a user model, cognitive architectures can also play a role in making the computer model smarter in interaction

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by helping them simulate a being with real cognitive abilities. An example of

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such simulations is found in agent simulated in computer games. However, they can also play an important role in more serious areas, for example making educational, research, and automation software products act as agents with senses.

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This will result in increased credibility and higher level interactions through increasing the sensory and cognitive capacities of the agents. In spite of what was said, there are still issues that seem to have received

less attention in the literature. These issues are summarized as follows: • Currently, the focus is on cognition and performance, and less attention is paid to user preferences, the fatigue resulting from the interaction, aesthetic elements, entertainment, etc.[62].

• Cognitive architectures have not been introduced to areas of social interaction such as teamwork and online communities. • A very basic problem that involves knowledge engineering of tasks and ways of interaction is that the production of each interaction requires 17

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the tasks to be analyzed and designed and the interaction is produced based on such analysis and design. With the introduction of cognitive

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architectures, since a new factor is added to the interaction environment, the design becomes more complex.

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• Implementation of modern cognitive architectures is a very difficult task.

• Most of the conventional interactions are closed source, and therefore,

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cognitive architectures cannot be incorporated into them.

• Most uses made of cognitive architectures so far have been focused on

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study and knowledge of the user model, rather than the production of a cognitive model for the computer side of the interaction. That is, we have failed to use the aid provided by cognitive architectures to increase the

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points of the interaction with a human. In other words, we have made our human-computer interaction system less intelligent.

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• For producing an interaction system, first the experts in an individual

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field make a statement of requirements. These requirements must then be analyzed, and a design must be made based on the results of the analysis by user experts. Afterwards, the designs are developed and implemented by another group of experts.

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• And finally, uses made of cognitive architectures so far have either had either an experimental nature or been limited to very special cases, as some cases were reviewed earlier. The question is now whether solutions can be found that allow ordinary end users in human-computer interaction systems to use cognitive architectures, and lead to all human-computer interaction systems being provided with significant levels of intelligence.

This paper tried to lay down the requirements of a potential solution that address the problems discussed above. The quest for making use of the achievements of cognitive sciences to find a way to eliminate the above-said disadvantage, while maintaining current advantages, will continue.

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4. Proposed Framework for Interaction Based on Cognitive Models

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and Architectures The optimized natural model of human-human interaction has been experienced over and over in the course of history, and codes of ethics and social

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rules have been created to regulate such interactions. As the new members of

the human family, computers can be highly beneficial to us. However, it should

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be remembered that these new members are supposed to serve the excellence of humans. It is alright to redesign humans life and work environment with respect to this new element, but it is completely wrong and against the rules of

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nature to do so while ignoring human nature, and failing to incorporate human features into computers while developing industrial artificial intelligence tech-

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nology. So, after several years of unrivaled dominance of the human-computer interaction literature, today, thanks to new computer technologies, many such researches have recently focused on the human features of such interaction, and

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on attempts to make such interaction increasingly human-centered[54]. This shift from task-centerism to human-centerism requires new methods

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of designing and developing human-computer interaction systems. To study these methods, it is better to first obtain a better knowledge of the features of the interaction between humans and the physical world. That is because

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this knowledge can be used to make computer interaction increasingly human, thereby, producing computers with the least adverse effect on humans. This way, we can build a future in which human-like intelligent agents serve humans and interact with the environment in almost the same way as humans do. 4.1. Expected Features in a human-computer Interaction System With the features discussed above in mind, we now look for a model of

interaction that meets these criteria and is able to play a role in human relationships. For this purpose, some of the features required in the future world of human-computer interaction are described as follows. These features have a direct effect on the design of human-computer interaction systems in future.

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• Perception: Artificial agents must have a very good perception of the physical world. They must notice the occurrence of events in their sur-

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rounding area with the aid of sensors and subsequently obtain a true

understanding of the physical world by perceiving the events. One of the

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most important components of the physical world is the user and his/her

behaviors. An artificial agent’s knowledge of the user is known as its model

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of the users behavior. Many remarkable works have been done on generating better sensors and better modeling of the user’s behavior[63][64]. • Automatic User Interface Generation: The communication between

an

a system and the physical world consists of two main parts. First, obtaining knowledge about the physical world, and second, reacting accordingly. In other words, the communication consists of inputs and outputs. In the

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previous section, the input, i.e. the perception of a system of its surrounding area was discussed, but it is the actually output that is seen from the

d

system. The product of all of the perceptions, logical deductions, and internal processes of the system is measured by the behavior of the system.

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Today, many human-computer interaction systems and conventional software products have pre-designed templates for their output, and only some contents of these pre-designed templates are modified to provide different

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outputs. It seems that future systems will be very flexible in providing outputs, exhibiting a target-based behavior according to the requirements of the problem, the user’s environment, and their knowledge of the user.

• Simple Editing: Editing an agent means making modifications to its internal model to correct its perception, behavior, or other features. For humans, except in case of disease treatment, editing is realized only through education, where the individual is provided with knowledge or experience so that he can notice an issue and take it into account in his behaviors. Today, generating systems that perform a particular set of tasks requires very complex processes of analysis, design and development that need to be carried out for each system separately. This is while if generat20

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ing human-computer interaction scenarios can benefit from education and learn from its interactions with expert in different fields, the process will

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be shortened and the interaction generation cost as well as the precision of the interactions outputs will significantly increase.

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• Communication with other Agents and Sharing Experiences: It

takes a long time and many interactions with the user for an agent to know

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him. After the perception matures, it can be used by other agents to raise their knowledge of a particular user. The model of a user is one of many instances of knowledge that can shared by agents. For humans, since

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interactive language is highly convergent with internal knowledge, such sharing of experience easily takes place. For human-computer interaction agents, if such an interaction is in the nature of the internal understandings

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of the agent, the agents can understand the language of knowledge of each other and significant synergy can occur between the agents.

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• Friendship with Humans: A human-computer interaction agents must

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act as a friend or as the agent of a particular user and handle many of his real world affairs based on its proper perception of that user, and the proper behavioral system it follows which is in turn based on the

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set objectives. This requires the character of the intelligent agent to be such that the human user believes it, and can trust it. Showing proper emotions in behavior highly contributes to the credibility of the agent’s character[65].

• Improvement of Audience: The ultimate goal sought by creating such an agent and establishing such interactions is to improve the level of perception, behavior, and life of the human user. This feature of future interactive systems will help many areas of human sciences to be incorporated in everyday technologies more concretely, and many scientific theories related to humans to be practically tested.

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4.2. Expected Features of Cognitive Agents

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Given the above-mentioned features for human-computer interaction systems and the introduction provided in the previous section on the application of cognitive architectures in human-computer interaction systems, we now in-

cr

tend to derive the requirements of a cognitive artificial agent that plays the role

of the user’s company as a multi-agent system in different interactions. Sup-

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ported by a cognitive architecture, such cognitive agents can provide some of the features mentioned above. In what follows the features of a cognitive agent are briefly discussed. These features are supposed to meet the heavy requirements

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of human-computer interaction systems.

• Perception and Interpretation of Inputs: The agent must be able

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to convert the inputs received from the physical world, raw sensors, or control interfaces into a perception of the physical world and a true interpretation of the model of the physical world in a hierarchical system. For

d

example, in a human-computer interaction system, such a reception comprises observing the user’s raw behaviors in interaction with the computer

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(movements of the mouse, the user’s eye movements, etc.), and then, converting this input into a perception of the environment, that is, guessing from the users eye movements which components of the image he has paid

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more attention to, or conjecturing from mouse movements what objective user’s behavior seeks, or how careful and attentive the user is.

• Harmonic Behaviors based on Internal Target and Belief and External Rules: For the purpose of credibility of the agent, its behaviors must be harmonious, so that a set of highly rational behaviors leads to a particular target. Such behaviors must be formed in consistence with the internal beliefs of the agent, and meet the rules governing the physical world. For instance, assume that there is an intelligent agent in a software interaction that is supposed to fill out a form with the aid of an external user. The internal target of the agent is to receive the data requested by the form. Based on its knowledge of the user, which constitutes part of its 22

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belief system, the agent must require the answers only to those questions for which it does not have the answers itself. On the other hand, based

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on various factors such as the data in question, (e.g. data related to the

user’s age, education, etc.), the features of the interaction environment

cr

(the web, a game environment, etc.), the manner in which the questions are asked, the arrangement of the questions, etc. the process can take very

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different forms. The harmonic behavior of the agent in the arrangement of the pages and collection of data from the user will manifest itself in the fact that it will prevent making the user tired of completing the form.

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• Learning how to perceive Behavior: The two features of the agent mentioned above, i.e. perceiving the inputs and outputting the right behavior, should be able to constantly improve in accordance with the fea-

M

tures of interaction environment, and the particular user. For instance, assume a case in which the agent receives a new behavior from the user’s

d

mouse cursor, which it had not experience before [e.g. idle movement of the mouse cursor on the visual component for a short while). Being able to

te

identify a new behavior and then labeling it based on subsequent behaviors means that the perception process has the ability to always expect learning new things. This is true of the agent’s behaviors as well.For instance,

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in the example of collecting the data specific to the user mentioned above, having interacted with a number of users, and having observed their behaviors, the agent must be able to provide better outputs that can improve the efficiency of interactions.

• Visibility of Belief: The agent’s inputs and outputs are connected to its rational core. The agent’s knowledge of its beliefs, the features of the physical world, the features of the users and different tasks, etc. all are embodied within this logic unit. If the internal logic of an agent is well interpretable and it has the capability to learn and modify its own internal beliefs, the designer of the interaction environment can create an agent for a particular purpose and upgrade its behavior merely by 23

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applying a very simple supervision. Moreover, this facilitates interaction between the agents for sharing experiences and beliefs, which in turn leads

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to the development of intelligent agents in different areas of interaction and communication.

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• Believable: To improve credibility of the agent as an amiable company,

many emotional components must be modeled in it. These emotions can

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influence the agents behavior, attention, and perceptions of inputs, in turn affecting its credibility.[65].

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4.3. Disadvantages and Concerns

Despite all of the advantages of human-computer interaction systems and agents with the above said features, new systems always have disadvantages,

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which should not be ignored. Such issues, if addressed appropriately, are sometimes preventable by regulating the systems, and sometimes by modifying the

d

designed systems.

• The Question of Content

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For all of the problems that require the establishment of an interaction system using intelligent systems, the most important question to answer is how to solve the problem. The designer must find an optimal solution

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to a problem, in a way that the solution meets different aspects of the requirements of the problem and the user. The interactive framework discussed in this paper does not provide a solution as to how to solve the problem as other frameworks do, but provides an instrument by which to generate the solution that is found using the framework.Although the aspects of the above intelligent framework takes details from preliminary design, and tries to generate many credibility and upgrading components by itself.

• Security of Personal Information Intelligent agents that accompany humans in different areas have access to many aspects of people’s lives. Confidentiality of these data must be 24

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among the most important priorities of future systems. Today the way large businesses use people’s personal data for purpose such as developing

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their business and predicting customers’ behaviors with regard to government policies are among the major threats associated with these technolo-

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gies. It is therefore important to find solutions that can guarantee that

the intelligent agent protects the users privacy and his personal informa-

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tion, and whenever it is required to share data in interaction with other systems, it does so taking into account the security level of the data and the level of abstraction of its representation.

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• Thinking Assistant vs. Substitute for Thinking

One of the most important and least discussed threats associated with these systems is violation of the user’s dignity and affecting his mental

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capabilities. Consider the case of computer search features, for example. At first glance, this feature seems to help a user find the content he is

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looking for more rapidly and focus on thinking instead. However, after the user uses this feature, he starts to alter his thinking mechanism to rely

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on the computers searching capability instead of using the search feature for the purpose of better thinking. Today, threats to the users’ cognitive capabilities are found in many new systems. It is therefore crucial to try

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to design systems in which intelligent agents serve the thinking process of the users, and invite them to use their intellectual capabilities in different areas, rather than intruding and interrupting their thinking process.

5. Conclusion

In this study we developed a request for proposal to introduce cognitive

agents as assistants for futures studies researchers. We also attempted to review some of the previous leading methods that used cognitive agents in humancomputer interaction. Popper’s diamond [2] categorizes different methodologies of foresight based on how they utilize evidence, interaction, expertise and creativity. We suggested 25

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an approach relying on computational cognitive agents to assist future studies researchers. These agents have access to a huge amount of data and evidence on

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the web. They are able to communicate with each other in a higher abstraction

level of knowledge, which allows them to share their ideas and beliefs. They have

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a powerful user model and the capacity to perceive the behavior of the experts,

making them credible human like assistants for experts who understand their

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visual logic. Finally, using symbolic cognitive models, these agents are able to have imagination, emotion, and humanly plausible logical deduction systems. These abilities make them qualified agents for negotiating with users, discussing

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their creative ideas, and eventually improving their ideas. We have examined various requirements of future computational cognitive agents and identified some of their key features. Among the most important required features for

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such agents are the ability of comprehension and interpretation of the behavior of the user and the physical world, having harmonious and target-based actions, a system of internal beliefs, an interpretable internal logic, and external rules.

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