Domain modeling in computational persuasion for behavior change in healthcare

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To illustrate the ability of this model to reproduce the general characteristics of experimental data, we simulated the results of recently published experimental data from a study by Spring et al.

A sample of simulation results based on this data is shown in Fig. The red solid line represents the average daily activity in minutes, the black dashed line represents the Type 1 system, and the blue dotted line represents the Type 2 system. During the same time, the Type 1 process expertise is learning slowly, but takes over after the coaching ceases. Simulated data from Spring et al. Our most recent evaluations of the Health Coaching Platform have tested the integration of data from sensors in the home: computational models that infer patient state, context, and adherence to goals; and intervention protocols to promote health behavior change.

For our intervention protocols on cognitive training, sleep management, socialization, and physical exercise, we first use an in-home visit or Skype conferencing to assess current activity levels, health behavior goal selection, readiness to change, motivations, and barriers when appropriate.

For example, with our sleep intervention, we assessed sleep hygiene issues, anxiety, and circadian rhythm patterns before recommending changes to the environment or relaxation exercises. A tailored action plan was created and updated each week. A total of 33 elderly participants average age As an example of monitoring various types of data from the home and providing feedback for a tailored health intervention, our socialization module for older adults in the home focuses on several proxy measures for inferring degree of socialization: time and contacts on email, time and contacts on the phone, time and contacts on Skype all older participants and an enrolled remote family member use Skype TM , and inferred time out of the home.

To assess a baseline degree of socialization at the beginning of an intervention, we use the Ludden social network scale-revised [ 85 ] and the UCLA-R loneliness scale [ 85 ]. For coaching purposes, we also use motivational interviewing to assess motivations, barriers, and readiness-to-change for each selected activity.

The feedback and recording of their participation in the intervention is based on computer, phone, or motion sensor data from the home or self-report for the in-person activities. With the subset of our coaching participants that chose a socialization intervention nine participants, At baseline, only two-third of our participants was completely satisfied with their level of socialization.

Our goal was to be able to provide a socialization intervention using Skype that could improve their social network and time interacting with people. All participants studied improved their level of socialization and continued to see a benefit in the maintenance phase of coaching after the nine weeks from using Skype to communicate with remote family members and friends. Weekly overall Skype usage in minutes by participants in the Socialization Intervention. The colors represent different participants.

One of the challenges demonstrated by this example lies in keeping adherence and motivation high throughout a lifestyle intervention. Maintaining health behaviors is often harder to accomplish than a short-term change. One of our approaches has been to offer a menu of weekly activities for variety and challenge, as well as multiple intervention topics. The integration of estimates of patient state, context, and adherence to goals provides a valuable framework for providing tailored, timely, and potentially scalable health behavior change interventions.

We have demonstrated the ability to integrate data science with models of sensors, activity, context, and behavior change into a coherent health coaching platform and deliver interventions in a way that keeps participants motivated and engaged. However, there are still many issues needing further attention.

With special populations, such as older adults, ease-of-use and meaningfulness of messaging remains a top priority. Expanding use to minority and underserved populations will also need testing and design input. We currently have limited approaches to modeling privacy and data sharing preferences.

The informed sharing of sensitive data requires insight that new participants often lack. We need to be able to estimate preferences, and do our best to share summarized data in a useful but privacy preserving manner. At the outset, we described a vision of behavioral informatics in which unobtrusive sources of information are used to monitor, assess, infer, and intervene to help people improve and manage their behaviors, consequently improving their health and quality of life.

Despite the advances in sensor technology, data science, computational modeling, inference, intervention optimization, and machine-assisted coaching platforms, there are many remaining challenges that need to be surmounted to implement this vision.

One of the outstanding problems stems from the variability across and within individuals in their health behaviors. Developing generalizable behavior inference algorithms that would not require a new training set for each new individual is inherently difficult. Several important health behaviors are still difficult to observe and quantify objectively and unobtrusively. These include diet, nutrition, and energy intake. Assessment of social and emotional aspects of behavior would also benefit from improved inference and assessment techniques.

Another important issue is false alarm fatigue. The generation of false alarms is a serious problem for behavioral informatics because of the continuous monitoring of low probability events. Under continuous or frequent monitoring, even well-performing detectors will likely generate a high proportion of false alarms.

For most detection tasks, this level of performance would be considered very good. Despite the detector performance, false alarm fatigue needs to be addressed before these systems can be routinely used. One intriguing approach to mitigating this problem is to incorporate the utility of detecting an important event like a fall versus the disutility of a false alarm in the decision process.

A closely related issue is the cost-benefit tradeoff of an intervention and participant adherence. This is intimately tied to the problem of discounting future benefits relative to an immediate cost. For example, if a coaching intervention is not perceived to have immediate benefits, the participating individual may not adhere to the shared goals and activities agreed to with the coach. The challenge of preventive healthcare, in general, is to increase the value of future benefits and convert them to more immediate gains.

Future work in this area will generate a set of tools and methods that will help improve the well being of all, including older adults, underserved populations, and those residing in rural areas and is likely to have a significant societal impact.

The proposed computational modeling-based approach to the assessment of behavioral, physical, cognitive, and affective states is expected to revolutionize healthcare delivery, including the provision of effective, timely, and targeted interventions. Further, the social benefits of behavioral informatics extend beyond healthcare, ranging from impacts in engineering disciplines, such as robotics, automation, and surveillance, to broad areas of education, economics, and scientific thrusts such as U.

Additionally, work in this new area of behavioral informatics will inspire a new generation of students, who will be able to address problems at the intersection of computer science, engineering, and behavioral and social sciences.

The main tenant of this paper is the notion that multiscale computational models ranging from sensors to behavioral decisions is an important prerequisite for optimizing interventions aiding individuals in learning and adhering to better health behaviors. In conclusion, we would like to note that this paper only begins to scratch the surface of the problem of helping people to improve their health behaviors.

Significant efforts in areas, such as sensor development, modeling, algorithm design, and clinical evaluations, are needed to address the complex issues in improving health behaviors. This work was supported in part by the U. The authors would like to thank S. Hagler, D. Austin, J. Kaye, T. Hayes, and many other colleagues at the Oregon Health and Science University and the Tampere University of Technology in Finland for contributions to this paper. They would also like to thank D.

Spruijz-Metz and W. Nilsen for valuable discussions, and M. Wang and K. Nikita for shepherding this paper through the preparation process. He was the Chair of the Department of Biomedical Engineering he founded in and as the Director of the Point of Care Laboratory, which focuses on unobtrusive monitoring, neurobehavioral assessment, and computational modeling in support of healthcare, with a particular focus on chronic disease and elder care.

His current fundamental research is at the intersection of multilevel computational modeling of complex behaviors of biological and cognitive systems, and augmented cognition. His most recent efforts are fundamental science and technology that would enable the transformation of healthcare to be proactive, distributed and patient-centered. Her research involves developing design principles for technology to enable patients to be more active and involved in their health care.

She is currently the Director of Northeastern. Among her key projects is tailoring health interventions based on feedback from in-home monitoring for diverse populations including older adults and those with neurological diseases. This research comprises computer modeling projects for assessing cognitive states using embedded metrics within adaptive computer games. Ilkka Kimmo Juhani Korhonen is a Ph. He has published more than 70 papers in international peer-reviewed journals, and more than publications in scientific peer-reviewed conferences.

Christine M. Gordon received the M. The focus of her work is on evaluating wearable and unobtrusive monitoring devices for tailored intervention development and designing user interfaces that convey meaningful health information through the integration and visualization of multiple data streams.

She has previously worked in research support with Brigham and Women. His research interests include personal health systems and eHealth, and innovation, technology transfer and technology policy setting in health technologies.

Holly B. Jimison, Northeastern University. Ilkka Korhonen, Tampere University of Technology. Gordon, Northeastern University. Author manuscript; available in PMC Mar Author information Copyright and License information Disclaimer. Corresponding author. Misha Pavel: ude. Copyright notice.

Abstract Health-related behaviors are among the most significant determinants of health and quality of life. Index Terms: Behavioral informatics, computational models, health behavior change, multiscale, self-management, wearable sensors. Introduction The rapidly increasing cost and limited effectiveness of healthcare is one of the most important societal and global challenges.

Open in a separate window. Closing the Loop With Intervention Protocols In healthcare and health psychology literature, there is considerable evidence that health interventions tailored to individuals are more effective than generic ones [ 10 ]—[ 12 ], and that timely feedback plays an important role in changing and sustaining behavior [ 13 ]. Models of Data Streams The first step in the assessment of behaviors includes a transformation from raw sensor data to useful information.

Computational Models of Sensors Sensing can be interpreted as a measurement process that associates numerical elements to attributes of a class of objects or events. Model-Based State Estimation From Physiological Sensors There are a variety of wearable sensors for collecting physiological measurements in the home, workplace, or general environment.

Model-based state classification based on beat-to-beat HR signal analysis. Models of Behavior Change The task of optimizing interventions can be interpreted as a control theory problem, where differences between observed behaviors and desired behaviors are used to generate the most effective input intervention , as outlined in Fig.

Linear Dynamical System Representation Rivera et al. Dual-Process Representation During the last couple of decades, behavioral researchers began considering a different class of models based on the dual-process theory [ 80 ], [ 81 ]. Integrating Monitoring and Modeling for Coaching Interventions Our most recent evaluations of the Health Coaching Platform have tested the integration of data from sensors in the home: computational models that infer patient state, context, and adherence to goals; and intervention protocols to promote health behavior change.

Limitations and Future Challenges We have demonstrated the ability to integrate data science with models of sensors, activity, context, and behavior change into a coherent health coaching platform and deliver interventions in a way that keeps participants motivated and engaged.

Conclusion Future work in this area will generate a set of tools and methods that will help improve the well being of all, including older adults, underserved populations, and those residing in rural areas and is likely to have a significant societal impact. Acknowledgments This work was supported in part by the U. References 1. Realizing the full potential of health information technology to improve healthcare for Americans: The path forward.

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Psychol Methods. Seto E, Bajcsy R. However, the modelling of emotional aspects of argument has received little attention in the computational argumentation literature.

There is a proposal for rules for specifying scenarios where empathy is given or received in negotiation [ 98 ], and there is a proposal for specifying argument schemas rules that specify general patterns of reasoning for capturing aspects of emotional argument [ 94 ]. In contrast, it is interesting to note that affective computing has put emotion at the centre of the relationship between users and computing systems [ 26 ]. So computational models of argument offer a range of formal systems for generating and comparing arguments, and for undertaking this in a dialogue.

However there are shortcomings in the state of the art of computational models of argument for application in persuasion. The current state of the literature does not adequately offer the following and hence there are some exciting research challenges to be addressed if we are to deliver computational persuasion.

A formalization of domain knowledge appropriate for constructing arguments concerning persuasion in application such as behaviour change e. For this, in Section 6 , I will discuss how asymmetric dialogues can be used. There are some promising proposals that could contribute to a solution e. However, if we are to harness some of the other levers of persuasion that I discussed in Section 3 , then we will need to broaden the modelling to incorporate aspects of personality and bias.

Strategies for persuasion that harness the persuadee model to find optimal moves to make at each stage trading the increase in probability of successfully persuading the persuadee against the raised risk that the persuadee disengages from the dialogue as it progresses. With this kind of information in the user model, we may be able to harness some of the levers of persuasion discussed in Section 3 such a persuasion techniques, framing style, and argumentation style.

I will discuss the progress we have made on this in Section 7. In order to focus research on addressing these shortcomings, we can consider how computational persuasion can be developed and evaluated in the context of behaviour change applications.

In order to have well-understood computational models of argument that correspond to human behaviour, there is a need to ground these models with studies with participants.

The studies undertaken so far validate some aspects of these models, but also indicate some shortcomings in being able to model human behaviour. Studies performed by Rahwan et al [ ] and Cerruti et al [ 32 ] investigated various forms of reinstatement in argumentation. The users were presented several argument graphs and were asked to explain how acceptable a given argument is in their opinion. The results show that in some cases, the implicit knowledge about domains can substantially affect the given acceptability levels.

Additionally, introducing the defense for this argument raises its acceptability. However, typically it does not reach the value of 1, which is the level the usual dialectical semantics would predict. In a study of argumentation dialogues, Rosenfeld and Kraus [ ] undertook an experiment in order to develop a machine learning-based approach to predict the next move a participant would make in a dialogue.

This work was further extended in [ , ]. The machine learning models were trained on data that incorporated the sequences of arguments in a dialogue that the participants accept. Once trained, the models were able to predict the acceptance an unseen case would have. In another machine learning-based approach, Huang and Lin [ 68 ] developed a software agent for participating in dialogues with potential customers with the aim of persuading them to offer a higher price for goods.

Dialogues were constructed from an argument graph, and training was done on simulated scenarios. In testing with users, the agent was able to persuade the participants to increase the mean price offer.

There are also studies with participants by Masthoff and co-workers that investigate the efficacy of using arguments as a way of persuading people when compared with other counselling methods indicating that argumentation may have disadvantages if used inappropriately [ ], and that rather than a confrontational approach, argumentation that is based on appeal to friends, appeal to group, or appeal to fun, may be more efficacious [ , ].

Emotion in argumentation has also be the subject of a study with participants in a debate where the emotional state was estimated from EEG data and automated facial expression analysis. In this study, Benlamine et al [ 15 ] showed for instance that the number and the strength of arguments, attacks and supports exchanged between a participant could be correlated with particular emotions of the participant.

For each type of problem, we can conceivably tackle a small proportion of cases with substantial benefit to individuals, government and society using techniques for behaviour change. Many organizations are involved in behaviour change, and many approaches are used to persuade people to change their behaviour including counselling, information resources, and advertising.

Many diverse factors can influence how such approaches can be used effectively in practice such as the following. Agenda e. I am always late for everything, and so I have to speed.

Opportunities to change behaviour e. Attitude to persuader e. I listen to Lewis Hamilton not a civil servant. There are persuasion technologies that have come out of developments in human-computer interaction research see for example the influential work by Fogg [ 49 ] with a particular emphasis on addressing the need for systems to help people make positive changes to their behaviour, particularly in healthcare and healthy life-styles. Over the past 10 years, a wide variety of systems have been developed to help users to control body weight [ 91 ], to reduce fizzy drink consumption [ 89 ], to increase physical exercise [ ], and to decrease stress-related illness [ 85 ].

Many of these persuasion technologies for behaviour change are based on some combination of questionnaires for finding out information from users, provision of information for directing the users to better behaviour, computer games to enable users to explore different scenarios concerning their behaviour, provision of diaries for getting users to record ongoing behaviour, and messages to remind the persuadee to continue with the better behaviour.

Interestingly, argumentation is not central to the current manifestations of persuasion technologies. The arguments for good behaviour seem either to be assumed before the persuadee accesses the persuasion technology e.

So explicit consideration of arguments and counterarguments are not supported with existing persuasion technologies. This creates interesting opportunities for computational persuasion to develop APSs for behaviour change where arguments are central.

Argument-based persuasion technology could complement other technologies by helping users when they contemplate change. This fits the technology into the Stages of Change model [ ] which comprises the following phases that someone might go through examples taken from [ ].

By acting at the contemplation stage, the user might be prepared to enter into a dialogue with an APS. The role of the APS would then be to provide context-specific personalized information to the user through arguments, and to handle the doubts and issues that the user might have in the form of counterarguments. In the next section, we consider the potential of this approach in more detail. A strategy for an APS needs to find the best choice of move at each stage where best is determined in terms of some combination of the need to increase the likelihood that the persuadee is persuaded by the goal of the persuasion, and the need to decrease the likelihood that the persuadee disengages from the dialogue.

For instance, at a certain point in the dialogue, the APS might have a choice of two arguments A and B to present. Suppose A involves further moves to be made e.

So choosing A requires a longer dialogue and higher probability of disengagement than B. Also suppose that if the persuadee engages to the end of each dialogue, then it is more likely that the persuadee believes A than B.

So if the APS is to make the best choice of move, it needs to consider both the risk and potential benefit from each of them. An APS should present arguments and counterarguments that are informative, relevant, and believable, to the persuadee.

If the APS presents uninformative, irrelevant, or unbelievable arguments from the perspective of the persuadee , the probability of successful persuasion is reduced, and it may alienate the persuadee. A choice of strategy depends on the protocol, and on the kind of dynamic persuadee model.

Various parameters can be considered in the strategy such as the preferences of the persuadee, the agenda of the persuadee, etc. So argument-based persuasion for behaviour change offers a challenging and worthwhile field for developing and evaluating computational persuasion. As indicated by the review of computational models of argument in Section 4.

Furthermore, there have already been some promising studies using dialogue games for health promotion [ 28 , 54 — 56 ], embodied conversational agents for encouraging exercise [ ], dialogue management for persuasion [ 5 ], and tailored assistive living systems for encouraging exercise [ 57 ], that indicate the potential for APSs. Computational models of argument drawing on ideas of abstract argumentation, logical argumentation, dialogical argumentation, together with techniques for argument dynamics and for rhetorics, offer an excellent starting point for developing computational persuasion for applications in behaviour change.

I assume that an APS for behaviour change is a software application running on a website or mobile device. Some difficult challenges to automate persuasion via an app are the following.

Need asymmetric dialogues without natural language interface : Since we cannot assume that we will have natural language interfaces that can cope with the diversity of language that might arise in the arguments and counterarguments in a behaviour change application, we need to develop alternative ways of getting information from the user.

This means that we will have asymmetric dialogues where the choice of moves available to the APS is different to the user.

This approach was also taken in dialogue games for health promotion [ 28 ]. Need short dialogues to keep engagement : If an APS uses too many moves in a dialogue, there is a substantial risk that the user will disengage from the dialogue. They will become bored or frustrated by the interaction, or they will run out of time. It is therefore imperative that the APS does try to keep the dialogue short and focused.

Need well-chosen arguments to maximize impact : If an APS uses arguments that are inappropriate for a particular user, we are at risk of alienating the user, and thereby losing in the attempt to persuade the user.

Need to model the user in order to be able to optimize the dialogue : So the APS needs to determine at each stage of the dialogue what the best choice of move should be. Need to learn from previous interactions with the agent or similar agents : If the APS is to have a useful model of a user, it needs to learn from previous interactions with the agent or similar agents.

Furthermore, if it is to anticipate the arguments the user might be entertaining at any stage of the dialogue, it needs to be aware of possible counterarguments. Simple example of an asymmetric dialogue between a user and an APS.

As no natural language processing is assumed, the arguments posted by the user are actually selected by the user from a menu provided by the APS. Here the user may select any number of the items on the list. The system also needs to handle objections or doubts represented by counterarguments with the aim of providing a dialectically winning position. To illustrate how a dialogue can lead to the presentation of an appropriate context-sensitive argument consider the example in Table 2.

In Fig. Arguments can be automatically generated from a knowledgebase. For this, we can build a knowledgebase for each domain, though there are many commonalities in the knowledge required for each behaviour change application. Behavioural actions e. Behavioural goals e. To represent and reason with the domain knowledge, we could harness a form of Belief-Desire-Intention BDI calculus in predicate logic for relating beliefs, behavioural goals, and behavioural states, to possible actions.

We could then use the calculus with logical argumentation to generate arguments for persuasion. A small example of an argument graph that we might want to generate by this process is given in Fig.

To support the selection of arguments, we require persuadee models. In this section, I outline a framework for computational persuasion that is being developed in an ongoing project for more information, see the project website 2.

For this, we have harnessed probabilistic argumentation. Two main approaches to probabilistic argumentation are the constellations and the epistemic approaches [ 74 ]. In the constellations approach, the uncertainty is in the topology of the graph see for example [ 43 , 72 , 92 ].

As an example, this approach is useful when one agent is not sure what arguments and attacks another agent is aware of, and so this can be captured by a probability distribution over the space of possible argument graphs. The usual definition for extensions grounded, preferred, stable, etc can be applied to each subgraph, and then for each subset of arguments X , the probability that X is an extension for the grounded respectively preferred, stable, etc extension is the sum of the probability of each subgraph that has X as a grounded respectively preferred, stable, etc extension.

In the epistemic approach, the topology of the argument graph is fixed, but there is uncertainty about whether an argument is believed [ 74 , 82 , 84 , ]. This is formalized by a probability distribution over the subsets of the set of arguments in the graph.

In addition, postulates have been proposed to capture intuitive constraints such as the rational postulate which states that if an attacker has a probability greater than 0. The epistemic approach has been extended with a probability distribution over subsets of the set of attacks [ ].

This is potentially useful for handling enthymemes. Since most arguments are presented in natural language, different agents may interpret them differently, and hence some agents may be belief that an attack holds between a pair of arguments whereas other agents might not.

We have undertaken studies with participants to evaluate how they deal with arguments arising in a dialogue [ ]. We asked each participant for their belief in the arguments at each stage of the dialogue, and whether they saw a negative i. For this study, we were able to make a number of observations including the following. Use of constellations approach. People may interpret statements and relations between them differently, and not necessarily in the intended manner, and hence a probability distribution over the possible subgraphs can capture this uncertainty.

Furthermore, people may explicitly declare that two statements are connected, however, they might not be sure of the exact nature of the relation between them. We therefore need to express the uncertainty that a person has about his own views, and this can be addressed with the constellations approach.

Use of epistemic approach. People may assign levels of agreement to statements going beyond the 3-valued approach of Dung; The epistemic postulates e. These points suggest the need for the epistemic approach to probabilistic argumentation.

Use of bipolar argumentation. The notion of defense i. This suggests the need for bipolar argumentation frameworks. We have also developed methods for acquiring crowd-sourced opinions on arguments, and shown how they can be used for predicting opinions on arguments [ 79 ]. We evaluated our approach by crowd-sourcing opinions from 50 participants about 30 arguments. This work shows how it is viable to acquire data from a number of contributers to construct classifiers, and that these classifiers can then be deployed to substantially decrease the number of questions that need to be asked of any particular user in a persuasion dialogue.

Given the potential for probabilistic argumentation to capture key aspects of uncertainty in the user model, I indicate below how strategic argumentation can be developed to harness the user model. Awareness of arguments For considering the uncertainty about the structure of the graph in the persuadee mind, we use the constellations approach.

Belief in arguments For considering the uncertainty about the beliefs of the persuadee, we use the epistemic approach.

The epistemic approach is useful for asymmetric dialogues where the user is not allowed to posit arguments or counterarguments [ 76 ].

The distribution can be updated in response to moves made posits, answers to queries, etc using different assumptions about the persuadee credulous, skeptical, rational, etc. The aim is to choose moves that will increase belief in positive persuasion goals or decrease belief in negative persuasion goals.

We have proposed methods for updating beliefs during a dialogue [ 76 , 77 , 80 ], for efficient representation and reasoning with the probabilistic user model [ 61 ], for representing uncertainty in the user model [ 78 ], and for harnessing decision-theoretic decision rules for optimizing the choice of arguments based on the user model [ 62 ].

Moves in a dialogue For considering the possible dialogues that might be generated by a pair of agents, a probabilistic finite state machine can represent the possible moves that each agent can make in each state of the dialogue assuming a set of arguments that each agent is aware of [ 75 ]. Each state is composed of the public state of the dialogue e. We can find optimal sequences of moves by handling uncertainty concerning the persuadee using partially observable Markov decision processes POMDPs when there is uncertainty about the private state of the persuader [ 60 ].

Disengagement For considering disengagement, we have investigated two options. The first is a simple Markov model that increases the probability of disengagement with each step of the dialogue [ 78 ].

The second is to use a weighting factor on the quality of a dialogue that is taken into account when we applying decision rules to construct an optimal policy [ 62 ]. Key possible dimensions for modelling uncertainty are summarized in Table 3. These developments offer a framework with a well-understood theoretical methodology, and implementations that are computationally viable for strategic argumentation.

The key research issues for our project are summarized in Fig. The aim is to address these issues in order to provide an integrated theory for applications in behaviour change.

Key aspects of our framework for computational persuasion. A solid arrow indicates a necessary flow of information whereas a dotted arrow indicates an optional flow of information. Domain model The content of the dialogues come from the domain model, and given that these are argumentation dialogues, the domain model needs to have the capacity to provide appropriate arguments and counterarguments as required.

In the simplest case, the domain model may be based on an argument graph, perhaps with meta-level information such as typing of arguments. A more sophisticated domain model might be based on a knowledgebase of logical formulae that can be used to construct arguments and counterarguments. User model The user model incorporates information about which arguments in the domain model that the user is aware of and information about the belief the user has in these arguments.

So information in the domain model is drawn on by the user model, and the user model uses the constellations and epistemic approaches to probabilistic argumentation to model these aspects. A user model can be acquired or predicted at the start of the dialogue, and it may be updated during the dialogue, and there are a number of options for this.

This may give accurate information but it may be boring to the user to be asked too many questions. For instance, we have developed methods for acquiring beliefs in arguments, and shown how naive Bayes classifiers can be trained to predict the belief in arguments for a given user [ ]. Strategy and dialogue There are options for the strategy as discussed in Section 7. The strategy draws on the domain model for arguments to use as content in the dialogue model, and it draws on the user model to determine the best choice of move at each stage of the dialogue.

The net result of the strategy is the system contribution to the dialogue. We have put uncertainty in arguments, in particular in belief in arguments, at the core of our framework for computational persuasion, as we believe this is a minimum necessary for persuasive behaviour.

However, we believe that other dimensions are highly desirable for a more comprehensive framework for computational persuasion. In particular, some of the dimensions considered in Section 3 are potentially valuable including rationality of arguments in particular quality of arguments and of argumentation , persuasion techniques, argumentation style, framing of arguments, and emotion of arguments.

Computational persuasion, being based on computational models of argument, is a promising approach to technology for behaviour change applications. Advantages of dialogical persuasion over unidirectional persuasion for behaviour change include:. Context-sensitivity A dialogue can take into account the context of the user.

Interactivity A user can provide input to help guide the content and approach to the dialogue, and thereby making them more engaging and useful for the user. Developing an automated persuasion system APS involves research challenges including: undertaking the dialogue without using natural language processing; having an appropriate model of the domain in order to identify arguments; having an appropriate dynamic model of the persuadee; and having a strategy that increases the probability of persuading the persuadee.

Furthermore, with even a modest set of arguments, the set of possible dialogues can be enormous, and so the protocols, persuadee models, and strategies need to be computationally viable. Persuasion can be described as a process for overcoming barriers to behaviour change.

There are many kinds of barriers. A simple dichotomy is that of informational barrier and psychological barrier.

So a user may hold incorrect information e. Such informational barriers to behaviour change are the primary focus of our approach to computational persuasion so far. This may include debilitating emotions, dysfunctional attitudes, and perceived invulnerabilities, and therefore addressing psychological barriers calls for more sophisticated approaches to the development computational persuasion.

In the short-term, we may envisage that the dialogues between an APS and a user involve limited kinds of interaction. For example, the APS manages the dialogue by asking queries of the persuadee, where the allowed answers are given by a menu or are of restricted types e.

Obviously richer natural language interaction would be desirable, but it is not feasible in the short-term. Even with such restricted asymmetric dialogues, it may be possible that effective persuasion can be undertaken, and furthermore, we need to investigate this conjecture empirically with participants.

In the longer-term, there are likely to be exciting opportunities for combining computational models of argument with computational linguistics for much more involved and convincing dialogical argumentation for persuasion. I am grateful to the anonymous reviewers for helpful feedback for improving the paper.

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