Modelling Optimal Human Resources Using Machine Learning

Transcription

1 Modelling Optimal Human Resources Using Machine Learning Santosh Shinde om Arti Dighe Chitralekha Jadhav ABSTRACT When a new complex project begins in industry, usually the project manager(s) is responsible of partitioning this project into tasks and selecting the people who will perform them. Project manager takes this decision based on his/her past experience or creativity level. The modelling of human behaviour in a work team used to analysis the specific dynamics behind individual and work team performance. This analysis supports the decision-making in the formation and configuration of real work teams in companies. To model human behaviour at work uses software agents in which human characteristics are represented by a set of fuzzy values, and fuzzy rules and optimized suitable team by knapsack algorithm. The work described in this paper presents a software agent-based simulation model that aims to support this decision-making process. Keywords Artificial Intelligence, human behaviour, Multi-Agent Systems INTRODUCTION As many new projects come in industry, the project manager(s) partition this project into subtasks and selecting the people who will perform them. The appropriate selection of people to configure a successful work team is not a trivial decision-making process [1] because of several complex factors that influence the individual and work team performance. Nowadays usually most of the team formation process is performed by the project managers, who based on their past experience and available information about personal and professional characteristics of the potential team members. The success of a project is greatly due to the personal expertise and responsibility of each member, but also due to the communication, collaboration and co-operation between the individual team members. Often, a good work team performance also depends directly on the personal characteristics of each team s member, such as social skills and personality traits making these human characteristics of vital importance in projects where the interaction and 14 communication between the team members are fundamental for the achievement of the final objective. Additionally, the emotional state of a person plays a critical role in rational decision-making, perception, human interaction, and human intelligence, affecting its own performance and the performance of the whole team during the project. We have proposed an agent-based model [3] to simulate the interaction of a team member with other team members and with the tasks of a project. In our model, we propose that each software agent represents a real person by configuring a set of relevant human characteristics at work within each agent. Several simulations can then be run with the hypothetical team to get estimates of the team performance in charge of a specific project. The model we are proposing uses fuzzy logic to represents the agent internal characteristics and some of the project characteristics. This paper focuses on these fuzzy characteristics and on how the team behaviour can be modelled from the agents and project characteristics also using fuzzy logic. HUMAN BEHAVIOUR AS MODEL Why to Model the Human Behaviour? The study and understanding of human behaviour has been one of the topics under discussion during many years. More recently, with the great development of the computing sciences, the modelling and simulation of different aspects that form the human behaviour has been an interesting area of research. The modelling and simulation of human behaviour is important mainly for four reasons: 1. It offers sufficient practice for human training. 2. It is a practical solution to improving readiness and lowering costs. 3. It can be used for conducting what-if scenarios.

2 4. Simulations of what actually occurred can be used for after-the-fact analysis. How to Model the Human Behaviour? Human Behaviour as a Complex System The complexity is increased when models of human behaviour include the representation of people interacting with others. Realistic models of human behaviour should include this important scenario because humans, as social beings, are highly influenced by the interaction with other humans during the every-day activities. Agent Based Modelling The key component in ABM is the concept of Agent, which is an autonomous software entity with the ability to interact (sociability) with other agents (including humans) and with the environment. Autonomy means that agents are active entities that can take their own decisions based on their own goals. An agent, however, will decide whether to perform or not a requested operation, taking into account its goals and priorities, as well as the context it knows. What to Model in the Human Behaviour? Modelling Human Capabilities at Work Modelling human behaviour is a great challenge due to human nature, i.e. humans are unpredictable, unstable, and capable of independent action. The performance of individuals will fluctuate depending not only on their ability, training and education, but also on their physiological and psychological states and traits [5]. Three main challenges in capturing complex patterns of human behaviour in agent based simulations have been identified in: a. Humans are not limited to one identity or any common set of emotions[6]; b. Humans are not limited to acting in accordance with predetermined rules; c. Humans are not limited to acting on local patterns. We propose that it is possible to simulate part of the human behaviour in the context of a work team in charge of a specific project. The first step to achieve this team behaviour simulation is to identify the set of relevant human characteristics that we consider affect the performance of a person in this specific context. They can be grouped into cognitive capabilities, personality trends, emotional states and social characteristics. Cognitive Capabilities: The cognitive capabilities of a person were defined as his/her degree of expertise in a particular domain. Thus, to represent the technical knowledge of a person within a team, a set of six cognitive classes was set: 15 Project Manager, Coordinator, Specialist Sr., Specialist Jr., Technician and Assistant. In addition, every team member has two other independent parameters: experience level and creativity level. Personality Trends: We have taken into account two different psychological approaches to identify the personality trends that influence the behaviour of a person when performing his/her work. The first approach is based on the CLEAVER technique [8], used to identify the predominant personality trend of a person. The CLEAVER technique is applied to the candidates through several questions about his/her likely actions in front of different work situations. The result of this questionnaire is a numerical value between 1 and 99 for each of the following personality trend parameters (DISC): Drive leadership; capability to achieve results, overcome challenges and display high initiative. Influence capability to interact with people and motivate them to improve their behaviour. Steadiness capability to follow routine and continuous activities without large variations in behaviour. Compliance capability to execute work following established rules and procedures. Four general personality trends that may influence behaviour of a person: Amiable, Driver, Expressive and Analytical. These four personality trends are closely related to the CLEAVER trend parameters: Drive Driver, Influence Expressive, Steadiness Amiable, and Compliance Analytical. Emotional State: From the large set of basic emotions [9], we select a small set of four basic emotions to model the agents emotional state at work. Two of them are positive emotions and the other two have a negative influence over performance: Positive emotions: Desire and interest of a person to execute a specific task in a given moment. Negative emotions: Disgust and anxiety generated by a specific task in a given moment. In addition to these four basic emotions, we also consider the stress parameter as part of the internal state of the agents. The stress is not an emotion, but its influence over the performance of a worker is recognised in several studies [10]. In our model, the difference between the basic emotions and the stress parameter is given when the behaviour of the agent is generated. Social Characteristics: Human relations are important to achieve a good communication and co-ordination among the group members. Introverted/Extroverted and Prefers to work alone/prefers to work in a team.

3 Once the characteristics that affect performance at work are identified, the next step is to determine how to model the GENERAL ARCHITECTURE When a project is started, the project manager selects a set of team members according to his/her own experience. Once the team is formed, different simulations of its behaviour are performed. If the overall results indicate that the team could possibly interaction between the internal characteristics of the different team members for the generation of overall behaviour. complete the project with success, then project manager save the team configuration in a file for future reference In this process, the important factor is the representation of the real candidates as software agents. The general architecture of our model is shown in Figure 1. Figure 1. General Architecture of the team configuration process MACHINE LEARNING PARAMETERS When describing human attributes and relationships, most of the times the people tend to use words such as low, medium and high. When a computational model has to deal with this type of variables one suitable method to represent their values is through the use of Fuzzy Logic. In conventional logic, a statement is either true or false, with nothing in between them. In Fuzzy logic, offers a better way 16 of representing reality by considering various degrees, ranging from completely true through half-truth to completely false. The very first step in the use fuzzy logic within our model is to identify the parameters that will be fuzzified and to determine their respective range of values. In proposed the idea of a fuzzy set, where objects can belong to the set with different degrees of membership. Agent internal characteristics

4 Every agent s internal characteristic is fuzzified by using a Gaussian-shaped membership function for its corresponding fuzzy set. For the emotion, cognitive and social characteristics, three intensity fuzzy sets were defined with a Gaussian membership function. The range of these values in these fuzzy sets ranges from 0 to 100 and from 0 to 1 in the x and y axes respectively. Axis x represents the different values that the attributes can get, and axis y represents the membership s degree of those attributes to each value. The first fuzzy set represents a low intensity where the range of values under the shape runs from 0 to 35. The second fuzzy set represents a medium intensity contains the range of values from 25 to 75. Finally, the third fuzzy set with the range of values from 65 to 100 represents a high intensity corresponding to the agents attributes. Figure 2. Fuzzy sets defined to represent the values for the emotional, cognitive and social related Attributes in the TEAKS agents. The values defined in the three fuzzy sets allow linguistic labels for the attributes with different degrees of membership to each of the three values that, in the case of the emotions, can be increased or decreased throughout the simulated execution of the project. The possible increment/decrement in the values of the emotions depends on the specific characteristics of the environment, i.e., the characteristics of the other team members and the particularities of the assigned task(s). We can obtain the crisp value corresponding to a set of fuzzy values by applying equation 1.1 to obtain the center of gravity (COG) [11]. Through the simulation process the crisp values of the increase/decrease fuzzy values are added to the crisp values of the intensity fuzzy values for the corresponding emotion according to the triggered rules. The result of this addition is then fuzzified to get the new emotional state of the agent. eq. (1.1) In equation 1.1 µcrisp is the defuzzified value; bi denotes the center of the membership function, and μ(i) denotes the area under the membership function μ(i). Task parameters The behaviour of the work team is modelled through the interaction between the team members and project tasks which are assigned to them. The project tasks must be also modelled for this reason. The project tasks modelled by setting the values to eleven selected task parameters: 1. Number of participants in the task. 2. Estimated duration (measured in days). 3. Sequence (sequential or in parallel). 4. Priority within the project. 5. Deadline. 6. Cost. 7. Quality. 8. Application domain. 9. Task description 10. Difficulty. 11. Type The last two parameters i.e. Difficulty and Type are fuzzy parameters. The Type parameter represents the required specialization level to achieve the given task. For both parameters, the fuzzy values range from 0 to 100 divided into three fuzzy sets: low_(type/difficulty) ranges from 0 to 35, medium_(type/difficulty) from 25 to 75, and high_(type/difficulty) from 65 to 100. These fuzzy task parameters will be used to generate the agent behaviour by firing fuzzy rules. Agent performance parameters As the correct selection of evaluation performance metrics largely depends on the type of work that will be assessed, the agent performance at work is evaluated by analyzing his/her capability to perform the assigned task as well as his/her interaction with the rest of team members. Both the ability in performing a task and the personal social skills affects the team performance. Some parameters have been proposed to assess the agent performance at work: 1. Goals achievement. 2. Timeliness. 3. Quality of the performed task. 4. Team collaboration level. 5. Individual contribution level. 6. Required supervision level. In our model, the fuzzy sets for the corresponding parameters use the same Gaussian-shaped membership function in each fuzzy set. For the member performance parameters the values ranges from 0 to 100, divided into five fuzzy sets: very_low (0-30), low (25-65), minimum (45-75), acceptable (65-95) and satisfactory (90-100). Figure 3. Fuzzy sets used to represent the agent performance Modelling human behaviour The fuzzy values that modify the timeliness parameters when the corresponding fuzzy rules are fired range from 20 to 20 with the following fuzzy values: high_advance( from 20 to 5), medium_advance(from 10 to 0), normal (from 5 to 5), medium_delay (0 to10) and high_delay (5 to 20). 17

5 Figure 4. Fuzzy sets for the timeliness performance parameter When all these values are defuzzified, then calculated crisp values are added to the corresponding parameters. In every simulation, when the fuzzy rules are triggered the value of each parameter can be increase or decrease. At the end of all the simulations every agent has values for each of its performance parameters with respect to each assigned task. TEAM OPTIMIZATION Now we have multiple teams with corresponding there weights or machine parameters, to decide which team will optimized. In this paper we used knapsack s Algorithm to find optimized team. Workflows as structures for automating and managing business processes using IT infrastructure help organizations to increase efficiency and reduce efforts. While approaches for quality-based selection of high-performance web services for workflow tasks, however, have been recently well studied, little has been done to address the other side of a successful and efficient workflow execution: The selection of eligible users that contribute to a dependable execution and quality results. On the other side, managers put a lot of effort into assigning their resources to tasks depending on employee capabilities and workload situation. An automated, qualityaware user selection can enable organizations to dynamically set up and manage multi-user executions of workflows as well as optimally utilize its human resources. A 0-1 knapsack problem is a combinational optimization problem where items with various weights and benefits are selected to fill a weight constrained knapsack. The objective of the problem is to maximize the value of the knapsack by selecting the most valuable items such that the sum of the items weight is less than the knapsack s fixed capacity. In the case of team optimization in company, the knapsack s weight constraint represents a team s machine parameters value. CONCLUSION We have applied fuzzy logic to model human behavior at work and set of selected human characteristics that influence the performance of people when assigned to a task on company project. We create fuzzy rules to model this human behavior and predict the possible performance of each person over his/her assigned task and a set of possible results from the performance of the whole team. REFERENCES [1] Picard, R.W. Affective Computing MIT. Press: Cambridge, MA [2] Wooldridge M. and Jennings N. R. Intelligent Agents: Theory and Practice The Knowledge Engineering Review, Vol. 10, No. 2, pp [3] Martinez-Miranda, J. Aldea A. and R. Baiares-Alcantara: A Social Agent Model to Simulate Human Behavior, in Proceedings of the 3rd. Workshop on Agent- Based Simulation, Christop Urban (Editor) pp April [4] A. Furnham, L. Forde and F. Kirsti, Personality and work motivation, Personality and Individual Differences 26, pp , [5] Richard W. P., A. S. Mavor (Editors) Human Behaviour Representation Military Requirements and Current Models, Modeling Human and Organizational Behaviors, National Academy Press, [6] C.F. Kurtz and D.J. Snowden, The new dynamics of strategy: sense-making in a complex and complicated world. IBM Systems Journal 42(3), pp , [7] D. Batten Simulating Human Behaviour: the Invisible Choreography of Self- Referential Systems. In Pahl- Wostl, C., Schmidt, S. and Jakeman, T. (eds) iemss 2004 International Congress: "Complexity and Integrated Resources Management". International Environmental Modeling and Software Society, Osnabrueck, Germany, June [8] Cleaver, P. Tres Tecnicas de Personalidad (Cleaver). (in Spanish) (2000). Editor: Sociedad de Psicología Aplicada, México, D.F. [9] Ortony, A., and Turner, T. J. What's basic about basic emotions? Psychological Review, Vol. 97, pp [10] Rob B. Briner and Shirley Reynolds. The costs, benefits, and limitations of organizational level stress interventions In Journal of Organizational Behavior Vol. 20, Issue 5, September 1999, pp [11] E. H. Mamdani and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, International Journal Machine Stud., Vol. 7 (1), [12] L. A. Zadeh, Fuzzy Sets. Information and Control, 8. pp ,