Belief-Desire-Intention (BDI) model is well suited for describing agent’s mental state.The BDI of

an agent represents its motivational stance and are the main determinant of agent’s actions. Therefore, explicit understanding of the representation and modelling of such motivational stance plays a central role in designing BDI

agent with successful behavioural change interventions.Nevertheless, existing BDI agent models do not represent agent’s behavioural factors explicitly.This leads to a gap between design and implementation where psychological

reactance has being identified as the cause of BDI agent behavioural change interventions failure. Hence, this paper presents a generic representation of BDI agent model based on behavioural change and psychological theories.The

objective of this proposed BDI agent model is to bridge the gap between agent design and implementation for successful agent-based interventions.The model will be realized in an

agent-based application that motivates children towards oral hygiene

Ab. Aziz, Azizi

Adegoke, Ojeniyi

Yusof, Yuhanis

UUM Repository

Designing a BDI Agent Model for Behavioural Change Process 1Ojeniyi Adegoke, 1Yuhanis Yusof, 2Azizi Aziz 1Human – Centred Technology Group Computational Intelligence Platform,  School of Computing, College of Arts and Sciences (CAS) Universiti Utara Malaysia (UUM) 2ITU-UUM CoE for Rural ICT Development Universiti Utara Malaysia (UUM) go4oje@yahoo.com, {yuhanis, aziziaziz}@uum.edu.my  ABSTRACT — Belief-Desire-Intention (BDI) model is well suited for describing agent’s mental state. The BDI of an agent represents its motivational stance and are the main determinant of agent’s actions. Therefore, explicit understanding of the representation and modelling of such motivational stance plays a central role in designing BDI agent with successful behavioural change interventions. Nevertheless, existing BDI agent models do not represent agent’s behavioural factors explicitly. This leads to a gap between design and implementation where psychological reactance has being identified as the cause of BDI agent behavioural change interventions failure. Hence, this paper presents a generic representation of BDI agent model based on behavioural change and psychological theories. The objective of this proposed BDI agent model is to bridge the gap between agent design and implementation for successful agent-based interventions. The model will be realized in an agent-based application that motivates children towards oral hygiene.   Keywords- Belief-Desire-Intention; BDI model; agent; behaviour change, behavioural change intervention, psychological reactance   I. INTRODUCTION  Research into BDI agents that are capable of rational behaviour has received a great deal of attention in recent years. This is due to many reasons, but perhaps the most compelling is the fact that BDI model comprises of philosophical model of human practical interaction and reasoning. In [1], researchers proposed a planning theory of intention where agent intentions are treated as elements of partial plans of action. The plans play basic roles in practical reasoning; roles that support agent decision activities over time and social communication.   Based on the work reported in [2], [3], [4] and [5], it is learned that none of these studies explicitly depicts agent mental state in behavioural change intervention activities. Also, these BDI models do not explicitly describe mechanisms for the agent factors to interact between each other in order to achieve behaviour change. Therefore, this paper presents a BDI agent model of behaviour change that analyse agent’s factors and deflect psychological reactance in agent-based application intervention.     II. The BDI Agent Model The BDI model is closely associated with intelligent agents. However the BDI agent does not possess some characteristics associated with intelligent agents. For example, it allows agents to have private beliefs, but does not force them to be private [6]. Also, in [7], it is pointed out that a BDI agent does not consider agent communication. Consequently, the BDI software model is an attempt to solve a problem that has more to do with plans and planning (the choice and execution thereof) than it has to do with the explicit understanding of mechanism that leads to agent action and plans. Thus, in behaviour change intervention, BDI agent focus more on agent plans and planning factors in achieving their target interventions.  In order to explicitly understand how agent can achieve successful behavioural change intervention, there is a need to explore on existing psychology theories of behaviour change and psychological reactance. Hence, the proposed BDI agent model is based on the proposed investigation where the analysis is presented in Table 1. Tthe BDI model is an integrated model based on the following theories and model namely Relapse Prevention Model (RPM), Trans-Theoretical Model (TM), Self-Efficacy Theory (SET), Self-Regulation Theory (SRT), Theory of Reasoned Action (TRA), Theory of Planned Behaviour (TPB) and Health Belief Model (HBM). These existing psychology theories can be divided into two main groups namely: Social Cognition Models and Stage Models [8]. Based on these theories, Figure 1 depicts the interaction of the identified agent’s factors that produce behaviour change. The agent’s belief is represented under the initial stage where information about the agent’s plan is conceived. This stage can also be a term information state where agent acquires knowledge and belief on its action. On the other hand, agent’s desire is represented in the reasoning stage where it is influenced by many other interplaying factors such as severity of the action, perceived benefit of the action to the agent and the nature of challenge the action is posing to agent’s plan and agent’s desire which form the reasoning stage of the agent. The action determinant stage house the agent intention and it is known as the deliberative state of an agent. This is the stage that depicts the action that an agent has chosen to perform.   Intentions are desires to which the agent is somewhat committed to and this commitment is represented in the agent’s self-efficacy.      Table 1: Concepts in BDI Agent Model  No Concept Formalization Description Related Theory 1 Ability  Ab The capability to perform a behaviour  SET 2 Behaviour Knowledge   Bk The knowledge about the behaviour  TM 3 Behaviour Task   Ba Nature of the behaviour  TPB, TRA 4 Social Influence  Si External factors that enable the behaviour  TPB, TRA 5 Attitude to Change  Ac Mental state  TPB, TRA 6 Challenge  Cg Perceived  obstacle or impediment  HBM, TPB 7 Motivation  Mv Desire to perform the behaviour  TM, HBM 8 Perceived Risk Pr Negative consequences of the behaviour   HBM, TPB 9 Perceived Benefit  Pb Positive consequences of the behaviour  HBM, TPB 10 Threat  Hr Perceived risk to perform behaviour  HBM 11 Intention to Change  Ic The Willingness to perform the behaviour  HBM, RPM 12 Dissatisfaction  Df Negative reaction toward the behaviour  HBM, TPB 13 Negative Thoughts  Ng Negative perception and belief about the behaviour  HBM, TPB 14 Self-efficacy Se The belief in one’s capabilities or ability to perform a target behaviour or action.   RPM, TPB, SET 15 Severity of Behaviour  Sb The strictness of the consequences of a behaviour or action. HBM 16  Performed Action Pc A state when the behaviour or action is obtainable  SET 17 Planned Action  Pa The authorization of the behaviour or action SET 18 Belief  Bf A psychological state in which an individual holds a conjecture or premise on the validity and truthfulness   of a behaviour or action TPB, HBM, TRA 19 Desire to Change Dc Emotional sense of longing or wishing to change  SRT 20 Consistency in Action Ca A state when the action or behaviour is obtainable continuously  RMP, TM 21 Action Reject  Ar A state when the behaviour or action is deflected  SET 22 Consistency Refusal in Action  Cr A state when the behaviour or action is deflected continuously  SET   Figure 1: The BDI Agent ModelIII. Formalization and Simulations  Figure 1 depicts factors interaction in the model while arrows denote causal dependencies of interplaying factors. The formalization of the model was done based on [9] and the following formalization was obtained with respect to time (t).  Instantaneous formalization:     Sb (t) = Ba(t) [1-(1-Ar(t))] Cg (t) = wc1 * Ab(t) + wc2 * Si(t) + wc3 * Mv(t)  Mv (t) = σmv (Ab(t) + Si(t) + Cg(t))  + (1- σmv) (Ac(t)) Ac (t) = [σac * Bk(t) +  (1- σac) * Bf(t)] [1-Ng(t)] Pr (t) = Sb(t) * [1-( σpr * Cg(t) + (1- σpr) * Pb(t))] Pb (t) = [Wpb1 * Ac(t) + W pb2 * Mv(t) +  W pb3 * Cg(t)] * (1-Pr(t))  Dc (t) = Bf(t) * [σDc * Mv(t) + (1- σDc ) * Pb(t)] Ic (t) = Dc(t) * [σIc * Se(t) + (1- σIc) * Ba(t)] Se (t) = Pb(t) * [1- Ng(t)] Ng (t) = σNg *Pr(t) + [ (1- σNg) * Se(t)] Hr (t) = σHr * Df(t) + [ (1- σHr) * Ng(t)] Pc (t) = [WPc1 * Pa(t) + WPc2 * Ic(t) + WPc3 * Se(t)] * (1-Ar(t))   Ar (t) = σar * [Df(t) + Hr(t)] +  [1- σar (Pa(t) + Pc(t))]  Temporal formalization: Df (t + ∆t) = Df (t) + σDf * [Ng(t) – Df (t)] * (1-Df(t)) * (Df(t) * ∆t) Ca (t + ∆t) = Ca(t) + σCa * [Pc(t) – Ca(t)] * (1-Ca(t)) * (Ca(t)*∆t) Cr (t + ∆t) = Cr(t) + σCr [Ar(t) – Cr(t)] * (1-Cr(t)) * (Cr(t)*∆t)  Whereas: W = Weight value and   ∑         = 1,  ∑           = 1, ∑          = 1 σ = Constant  ∆t= Change in time (t)  For all instantaneous formalization equations, the parameters represent the equalization of corresponded contribution towards the overall equations. In addition, parameters for temporal equations denote the contribution for change rate. The formal model was implemented in the numerical simulation environment Matlab as shown in Table 2. For instance, scenario one case 1 depicts agent with low Behavioural task (Ba) and high in Planned action (Pa), Ability (Ab), Society influence (Si), Behavioural knowledge (Bk) and Belief (Bf). These scenarios depict different condition for the BDI agent model. Figure 2, 3 4 and 5 illustrate the scenario simulation results. The simulation results display the fundamental uniqueness of each scenario cases. The established simulations noted that the agent model can account for behavioral phenomena found in psychology and sociology.   Table 2: BDI Agent Model Scenario Cases  Scenario 1 Scenario 2 Scenario 3  Case 1 Case 2 Case 1 Case 2 Case 1 Case 2 Pa High  Low  Low High  High Low  Ba Low High  High Low  Low  High  Ab High Low  Low High  Low High  Si High Low  Low High  Low High  Bk High Low  High  Low  High Low  Bf High Low  High  Low  High Low    IV. Discussion The BDI agent model scenario 1 depicts an initial sharp increment in both consistency in action and dissatisfaction while consistency in action was found to be leading dissatisfaction as shown in Figure 2.  Also, there was decrease in consistency refusal in action. However, at time step 250 where there was a switch from scenario case 1 to case 2, it is observed that dissatisfaction leads consistency in action and consistency refusal in action. Whereas consistency in action was lagging behind dissatisfaction and consistency refusal in action, this was obtained as time step increases. This implies that when an agent acquires scenario one case 1, its action will be characterized by a high consistency in action, reduced dissatisfaction and low consistency refusal in action whereas agent with scenario one case 2 will be characterized with a high dissatisfaction, increased consistency refusal in action and low consistency in action.   Similarly, the motivation, attitude to change and intention to change were found to be of high value under scenario one case 1 as compared to scenario one case 2. This can be said to be the reason why there is a decrease in consistency in action and increase in both consistency refusal in action and dissatisfaction. Hence, it can be argued that there is a linear relationship between motivation, attitude to change, intention to change and consistency in action whereas there is transpose relationship between motivation, attitude to change, intention to change, consistency refusal in action and dissatisfaction. This finding is noted to be consistent with the ones reported in [10], [11] and [12].  Scenario two as shown in Figure 3 shown a very sharp increment in dissatisfaction which was found to be leading consistency in action whereas consistency refusal in action was found to be decreasing. A switch to case 2 from case 1 at time step 250 revealed that there was a decrease in dissatisfaction and increase in consistency in action where consistency in action leads dissatisfaction as time step increases. However, consistency refusal in action was found to be constant even after the switch to case 2 from case 1. This implies that agent that possesses Scenario two Case 1 will be characterized by high dissatisfaction, slightly improved consistency in action and a low consistency refusal in action whereas agent with scenario two case 2 will be characterized by an improved consistency in action, slightly reduced dissatisfaction and low consistency refusal in action. Also it is a surprised to learn that agents that possess scenario two case 1 and scenario two case 2 will experience the same level of consistency refusal in action.     Furthermore, a comparison between scenario two; motivation, attitude to change and intention to change revealed that attitude to change was found to be high but remain constant from scenario two case 1 to scenario two case 2. This implies that there is direct relationship between consistency refusal in action and attitude to change in scenario two cases. On the contrary, motivation was found to largely increase from case 1 to case 2 which suggest a direct relationship between motivation and consistency in action. However, surprisingly intention to change was found to slight decrease from case 1 to case 2. These arguments support previous studies by [13] and [14].       Figure 2: Agent Scenario 1                                                                                        Figure 3: Agent Scenario 2  Figure 4: Agent Scenario 3   Under scenario three, case 1 generated a very sharp rise in dissatisfaction while consistency in action and consistency refusal in action decreases within the same level. A switch to case 2 produced a rise in consistency in action and decrease in dissatisfaction while consistency in action lends dissatisfaction. Also, consistency refusal in action was found to decrease further in case 2. However, a comparison in scenario three motivation, attitude to change and intention to change depicts that motivation remains constant in the two cases, intention to change only increased very slightly whereas attitude to change dramatically increased. Hence, these scenarios have clearly shown that the model can give an explicit interplaying relationship between agent’s behavior factor and resultant agent’s action.    V. Implementation The agent model described in the previous section will be used as a basis in developing a persuasive agent that motivates children towards oral hygiene. Figure 6 shows the interface of the agent-based application and the Hong psychological reactance scale [15] will be used in the evaluation process.    Figure 6: Agent-based Application for Oral Hygiene  VI. Conclusion This work presents the design of a BDI agent model for oral hygiene. The model depicts how agent’s behavioural factors interact between each other in order to achieve behaviour change. This model will assist agent-based intervention designers to further understand the mechanism behind agent-based behavioural change intervention. Hence, enable them to design a successful agent-based intervention that will be void of reactance.   However, the model does not cover every aspect of human behaviour because human behaviour is a complex interplay of factors that comprises of socio-demographic, cognitive, biological and environmental factors. Nevertheless, the model is specifically tailored to software agent. It will guide designers to fully comprehend the factors that will enhance successful and efficient intervention for their designs. Further studies that will be done on this model include model verification via Temporal Trace Language (TTL) and developing the complete agent-based application to validate its usefulness.     Reference [1]. M. E. Bratman. Intentions, Plans, and Practical Reason, Harvard University Press: Cambridge, MA, 1987. [2]. C. Bisconti, A. Corallo, L. Fortunato and A. A. Gentile, Quantum-BDI Model for Information Processing and Decision Making. International Journal of Theoretical Physics, 2014, 1-17. [3]. M. Okaya and T. 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Designing a BDI agent model for behavioural change process

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Designing a BDI agent model for behavioural change process

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