In the previous chapter we have introduced and explained the TAC SCM game scenario.
In this chapter, we present the related work in literature with respect to TAC SCM.
The trading agent competition (TAC)is one of the few successful attempts by the AI community to devise competitive game scenarios that focus the energies of the research community on challenging problems [5, 6]. Such competitions can be an effective way to promote and drive research in complex domains and achieve better understanding, free of the complexities and risk of operating in real-world environments. [7] discusses its significance in the context of tackling challenging problems in today’s global networked economy as well as AI research, and some of the advances it has lead to over the years.
TAC SCM is a classic optimization problem with constraints and uncertainty. It presents a complex dynamic scenario, capturing the various challenges of supply chain management. There are three key issues in TAC SCM scenario that an agent needs to address: dealing with considerable uncertainty in a highly dynamic market, while competing with other self-interested strategic agents [8]. Developing an agent is a correspondingly difficult task.
Several teams from top institutes and research communities around the world have participated in the competition. In [9], the authors present the response from an informal survey which they sent to the TAC SCM community in 2007. They provide an overview of the design choices made and the specific architectural emphases the teams identified in their agent designs.
Most of the agents use multi-agent architecture to tackle the problem. They divide the problem into a number of sub-problems and solve these sub-problems using different appro...
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...he TacTex agent bases its actions on its predictions regarding the future of the economy in the game. The Botticelli team
[16] shows how the problems faced by TAC SCM agents can be modeled as mathematical programming problems, and offers heuristic algorithms for bidding on RFQs and scheduling orders. PhantAgent uses heuristic approximations to solve the sub- problems [41]. Mertacor [42] exploits the integration of techniques from the Operations
Research (OR) literature, heuristics and adaptive algorithms, as well as statistical modeling. CrocodileAgent [43] is built on the IKB (Information, Knowledge, and Behavior) model [44], a three layered agent-based framework. MinneTAC uses a component based framework [9]. All data to be shared among components are kept in the Repository, which acts as a blackboard [45]. Other agent architectures are described in [31, 46–49].
The two videos’ main topic is Keynote Ginni Rometty’s speech about IBM Watson and the impact of cognitive computing. The video role is Keynote Ginni Rometty who is IBM company CEO. Summarize her speech; I can understand three impacts about Watson, which are the AI for business, Watson cognitive computing change enterprise and Watson transforming industries.
Oppy, Graham, Dowe, David, The Turing Test, The Stanford Encyclopedia of Philosophy (Summer 2003 Edition), Mar. 10, 2005 .
Technology." Communications of the ACM 44.3 (2001): 88-91. Academic Search Complete. EBSCO. Web. 11 May 2011.
Artificial intelligence folklore has been traced back to the times of Ancient Egypt. But the "birth of artificial intelligence" as some would call it, was in 1956 at the Dartmouth conference. The conference was based on two theories, the principle of feedback theory and the Logic Theorist. The principle of feedback theory was observed by Norbert Wiener. He theorized that all intelligent behavior was the result of a feedback mechanism. An example would be a temperature control system that simply checks the temperature of the room, compares the reading to the desired temperature, and adjusts the flow of heat to bring the room to the desired temperature. Then in 1955, Newell and Simon developed The Logic Theorist. The Logic Theorist was a program that represented every problem as a tree. The program would attempt to solve a problem by selecting the branch that would most likely result in the correct solution. Then in 1956, John McCarthy1 organized the Dartmouth Conference to draw interest and talent to the field of artificial intelligence.2
Before we start, we would like to briefly introduce the definitions of Supply Chain and Supply Chain Management (SCM).
Throughout the article, Ford uses data to provide information on the current state of unemployment. He notes that from analysis, it was determined that the U.S. market is highly polarized from existing technology. Because of this polarization, it will be challenging for workers to find new jobs, if AI becomes more widespread and used. Ford concludes that, “it becomes somewhat difficult to imagine just what jobs might be left for even the most capable human works” if AI ever matches or exceeds human intelligence.
Supply chain management has been defined as that process that involves the management of information, materials, and all the finances that are handled within and across the entire supply chain process (Christopher, 2016). The management is usually done through out the entire supply chain management from that moment when the suppliers are involved through all the manufacturing activities, different distribution activities, and the way that the products are served to the final product consumer (Turban, et al., 2002). The process also includes all the activities that different organizations offers to their customers as after sale services for purposes perfecting their services and products towards their highly valued customers (Christopher,
...ast predictions of how fast AI will progress turned out to be misleading. This does not however mean that it has completely failed. Rather, it means that there was mere misunderstanding of the problems in those predictions involving the sciences and engineering. The future advancement of AI will require only the brightest of minds from many fields that include sciences, engineering, neurosciences, linguistics, philosophy, psychology, and most importantly, mathematics. Progress will be achieved as long as humans keep their imaginations and desire to achieve goals because with AI it is not only difficult, but also exciting (Sloman, 2009). In the 21st Century, Artificial Intelligence research will aim to add reasoning and knowledge to its existing applications, which makes it smarter, easier to use, more flexible, and increase its sensitivity to environmental changes
Enable their team with the allocation of customer relationship management system that best fits the requirements of the company in order to access to energy solutions through an online database to manage their CRM system.
The theory of constraints (TOC) is a systems-management philosophy developed by Eliyahu M. Goldratt in the early 1980s. The fundamental thesis of TOC is that constraints establish the limits of performance for any system. Most organizations contain only a few core constraints. TOC advocates suggest that managers should focus on effectively managing the capacity and capability of these constraints if they are to improve the performance of their organization.
It is a type of artificial intelligence program that imitated the analytical skills and understanding of human experts. By 1985, the artificial intelligence market had come up to one billion dollars; moreover, around the same time, Japan’s fifth generation computer project motivated the British and American government to bring back funding for artificial intelligence. Unfortunately, the artificial intelligence market fell back into disrepute which started with the fall of the Lisp Machine market. Additionally, this was a much longer “AI winter”. Soon, in the late 1900s and in the beginning of the 21st century, artificial intelligence was starting to be utilized for data mining, medical diagnosis, and in other areas as well as logistics. All this success was because of the increasing computational power, new relationships between other fields and artificial intelligence, higher significance on answering specific issues, and a commitment by researchers to scientific standards as well as mathematical methods. For example, on May 11th, 1997, Deep Blue (an IBM computer) was the first computer that played chess and it beat the ruling world chess champion at that time, Garry Kasparov. This was the beginning of an amazing discovery, artificial intelligence. Faster computers, able to obtain huge amounts of information, and statistical and advanced methods allowed progress in perception and machine learning. By the midyear of 2010, machine learning programs were utilized in the entire world. For example, Watson (IBM’s question answering system) beat Ken Jennings and Brad Rutter, the two greatest champions of Jeopardy, in a Jeopardy exhibition match by huge amounts. Another example is of the Kinect. It gives a 3D body-motion interface for the Xbox One and the Xbox 360 using algorithms that surfaced from long artificial research. Soon, 2015 came. According to
The ability to solve any problem no matter how big or small, it measures the intelligence in many different contexts. Intelligent machines cannot really serve any practical purpose unless the computers could cope with the big problem, which people overcome as a matter of routine. AI is a new step that is very helpful to the society. Machines can do jobs that require detailed instructions followed and mental alertness. AI with its learning capabilities can accomplish those tasks but only if the world’s conservatives are ready to change and allow this to be a possibility. There are many capabilities of AI. The more use we get out of the machines he less work is required by us. There would be less injuries and stress to human beings. Human beings learn by trying so we must be prepared to give a chance to AI. There is always that fear if AI is learning based, will machines learn that being rich and successful is a good thing? There are so many things that can go wrong with a new system so we must be as prepared as we can for this new technology. Research into the areas of learning, of language and of sensory perception has aided scientists in building intelligent machines.
Coyle, J., Langley, C., Gibson, B., Novack, R. and Bardi, E. (2008).Supply Chain Management: A Logistics Perspective. 8th ed. Cengage Learning, p.366.
If nineteenth century was an era of the Industrial revolution in Europe, I would say that computer and Information Technology have domineered since the twentieth century. The world today is a void without computers, be it healthcare, commerce or any other field, the industry won’t thrive without Information Technology and Computer Science. This ever-growing field of technology has aroused interest in me since my childhood.
My job at TCS has exposed to the various nuances of Supply Chain Management and therefore, I feel I can add value to my profession through a pursuit of this course.