Critical Analysis of Decision Making Experience with a Machine Learning Approach in Playing Ayo Game

The major goal in defining and examining game scenarios is to find good strategies as solutions to the game. A plausible solution is a recommendation to the players on how to play the game, which is represented as strategies guided by the various choices available to the players. These choices invariably compel the players (decision makers) to execute an action following some conscious tactics. In this paper, we proposed a refinement-based heuristic as a machine learning technique for human-like decision making in playing Ayo game. The result showed that our machine learning technique is more adaptable and more responsive in making decision than human intelligence. The technique has the advantage that a search is astutely conducted in a shallow horizon game tree. Our simulation was tested against Awale shareware and an appealing result was obtained

An Endgame Procedure for Generating Integer Sequence

In playing Ayo game, both opening and endgames are often stylized. The opening is very interesting with both players showing skills by the speed of their movements. However, there exists an endgame strategy in Ayo game called Completely Determined Game (CDG) such that its usefulness for ending a game should be apparent. In this paper, we present the CDG as a class of endgame strategy and describe its configuration and detailed analysis of its winning positions that generates integer sequence, and some self-replicating pattern

Effective energy consumption scheduling in smart homes

Abstract: Monthly expenditure on electricity by most households in South Africa take beyond acceptable percentage of their income. In order to keep the household energy expenditure below the energy poverty threshold, a daily electricity optimization problem is formulated using mixed integer linear programming (MILP) method. The energy optimization scheduling was carried out by a device called the Daily Maximum Energy Scheduling (DMES) device proposed to be incorporated into smart meters of households. The DMES algorithm was tested with household data set and was shown to be capable of ensuring that households spend less than 10% of their income on electricity bill monthly. This technique therefore, would be beneficial to consumers (for better financial savings and planning), utility (for effective energy and financial savings, and energy network planning) and cleaner environments as proposed for smart grid. Also, number of households in the nation living below the energy expenditure-based poverty threshold would increase

The lateral and ventromedial prefrontal cortex work as a dynamic integrated system:evidence from FMRI connectivity analysis

Recent functional magnetic resonance imaging (fMRI) investigations of the interaction between cognition and reward processing have found that the lateral prefrontal cortex (PFC) areas are preferentially activated to both increasing cognitive demand and reward level. Conversely, ventromedial PFC (VMPFC) areas show decreased activation to the same conditions, indicating a possible reciprocal relationship between cognitive and emotional processing regions. We report an fMRI study of a rewarded working memory task, in which we further explore how the relationship between reward and cognitive processing is mediated. We not only assess the integrity of reciprocal neural connections between the lateral PFC and VMPFC brain regions in different experimental contexts but also test whether additional cortical and subcortical regions influence this relationship. Psychophysiological interaction analyses were used as a measure of functional connectivity in order to characterize the influence of both cognitive and motivational variables on connectivity between the lateral PFC and the VMPFC. Psychophysiological interactions revealed negative functional connectivity between the lateral PFC and the VMPFC in the context of high memory load, and high memory load in tandem with a highly motivating context, but not in the context of reward alone. Physiophysiological interactions further indicated that the dorsal anterior cingulate and the caudate nucleus modulate this pathway. These findings provide evidence for a dynamic interplay between lateral PFC and VMPFC regions and are consistent with an emotional gating role for the VMPFC during cognitively demanding tasks. Our findings also support neuropsychological theories of mood disorders, which have long emphasized a dysfunctional relationship between emotion/motivational and cognitive processes in depression

An Empirical Judgment of Computer Simulated Ayo Game for Decision Making

Decision making plays an important role in the life of every living creature. Virtually on daily basis, people must make one or more decision. A faulty decision can lead to defeat in any competition. This paper presents the process of making decisions on the basis of knowledge of game playing as a major key in defining human characteristics. We simulated Ayo game playing on a digital computer and empirically evaluated the behavior of the prototype simulation. Empirical judgment was carried out on how experts play Ayo game as a means of evaluating the performance of the heuristics used to evolve the Ayo player in the simulation. A paper-based questionnaire was designed and administered to the Ayo game players which were used for the assessments of players’ perceptions of the prototype simulation, which gives room for statistical interpretation. This projects a novel means of solving the problem of decision making in move selections in computer game-playing of Ayo game

FORECASTING DISTRIBUTED DENIAL OF SERVICE ATTACK USING HIDDEN MARKOV MODEL

Distributed denial of service (DDoS) attack bombards the network with loads of packets and requests that consumes the system resources in terms of time, memory, and processors. This paper presents a proposed method for forecasting DDoS in networks. The proposed model employs hidden Markov model (HMM) to forecast DDoS attacks. The method uses the inherent characteristic features of DDoS to determine the observable states of the system.To avoid intractable computations, Kullback-Leibler divergence algorithm was employed to reduce the number of observable states to three. The proposed model is formulated and trained through experiments using DARPA 2000 data set and the preliminary resultsshows that the characteristic features of the DDoS and the entropy concept can be used to formulate an HMM to predict DDoS

FORECASTING DISTRIBUTED DENIAL OF SERVICE ATTACK USING HIDDEN MARKOV MODEL

Distributed denial of service (DDoS) attack bombards the network with loads of packets and requests that consumes the system resources in terms of time, memory, and processors. This paper presents a proposed method for forecasting DDoS in networks. The proposed model employs hidden Markov model (HMM) to forecast DDoS attacks. The method uses the inherent characteristic features of DDoS to determine the observable states of the system.  To avoid intractable computations, Kullback-Leibler divergence algorithm was employed to reduce the number of observable states to three. The proposed model is formulated and trained through experiments using DARPA 2000 data set and the preliminary results shows that the characteristic features of the DDoS and the entropy concept can be used to formulate an HMM to predict DDoS

Supply chain optimization towards personalizing web services

Personalization, which has the ultimate goal of satisfying user’s requests, can be perceived in terms of QoS measurement. As one of the means for the success of Semantics Web, many techniques have been effectively used in modeling and developing web service personalization. However, most of these methodologies relied heavily on detailed implicit and explicit information supply by users during initial and subsequent interactions with the systems. We propose in this paper a novel approach using the supply chain management (SCM) technique in personalizing web services as against the conventional notion of applying SCM only to product manufacturing. Our user-model based framework uses multi-agent system (MAS) components in taking requests from users and working towards their satisfaction including seeking for additional information outside the system as the need arises. Only basic stereotype information furnished by potential users at initial contact is required for personalization during subsequent interactions with the system. The system is adaptive and aimed at high quality autonomous information services where users are successfully presented preferred web services with minimum information request

Reviewing the decision-making behaviour of Irrigators

The contribution of agriculture to society is undeniable, as is its impact on the environment. Irrigators' decisions to follow best management practices or implement a policy change, to accept a technology, or even to exit farming, all affect society. Hence the decision‐making behavior of irrigators is of interest to politicians, policymakers, and researchers due to their impact on resource use and social concerns for their welfare. There are numerous studies available regarding the decision‐making behavior of irrigators. Most of them concentrate on decisions within a single time frame, single decisions with multiple driving forces, or multiple decisions with a single driving force. We have conducted a comprehensive review of the existing literature related to irrigators' decision‐making behavior. We used a systematic method to identify relevant publications and used qualitative data analysis (content analysis) to analyze trends and/or patterns across the selected articles. This research provided a typology and an overarching high‐level framework of irrigators' decision‐making process irrespective of the types of decisions made. The results of the study demonstrate that it is highly beneficial to integrate both qualitative and quantitative methods in a single study to get a complete picture of irrigators' decision‐making process. This allows us to ensure that we have captured the relevant drivers of decision‐making in highly dynamic and complex environments. Better knowledge of irrigators' decision‐making process allows regulators to shape improved agricultural policy and increase acceptance by irrigators of technologies that allow water managers to allocate resources fairly among different stakeholders.Lubna Meempatta, A. James Webb, Avril C. Horne, Louise Anne Keogh, Adam Loch, Michael J. Stewardso

Facial expressions depicting compassionate and critical emotions: the development and validation of a new emotional face stimulus set

Attachment with altruistic others requires the ability to appropriately process affiliative and kind facial cues. Yet there is no stimulus set available to investigate such processes. Here, we developed a stimulus set depicting compassionate and critical facial expressions, and validated its effectiveness using well-established visual-probe methodology. In Study 1, 62 participants rated photographs of actors displaying compassionate/kind and critical faces on strength of emotion type. This produced a new stimulus set based on N = 31 actors, whose facial expressions were reliably distinguished as compassionate, critical and neutral. In Study 2, 70 participants completed a visual-probe task measuring attentional orientation to critical and compassionate/kind faces. This revealed that participants lower in self-criticism demonstrated enhanced attention to compassionate/kind faces whereas those higher in self-criticism showed no bias. To sum, the new stimulus set produced interpretable findings using visual-probe methodology and is the first to include higher order, complex positive affect displays