Decentralized Problem Solving in Logistics with Partly Intelligent Agents and Comparison with Alternative Approaches

1996 ◽  
pp. 87-103 ◽  
Author(s):  
Peter Mertens ◽  
Jürgen Falk ◽  
Stefan Spieck ◽  
Mark Weigelt
Keyword(s):  
Problem Solving ◽  
Intelligent Agents ◽  
Alternative Approaches

Alternative Approaches to Auction Trading by Consortia in Multi Agent Systems

2013 ◽  
pp. 1-14
Author(s):  
Barin N. Nag ◽  
Dong-Qing Yao ◽  
Sungchul Hong
Keyword(s):  
Intelligent Agents ◽  
Heuristic Methods ◽  
Multi Agent Systems ◽  
Management Activity ◽  
Chain Management ◽  
Agent Based ◽  
Agent Systems ◽  
Multi Agent ◽  
Alternative Approaches ◽  
The Impact

Agent-based auction trading is important in e-Procurement as a part of the supply chain management activity of procurement via the Internet. Participating buyers and sellers are intelligent agents tasked with finding matches with required or offered quantities for best performance. Formation of consortiums offers opportunities in matching trade volumes, but in the real world, there are difficulties in optimizing consortium formation due to lack of perfect information and the dynamic character of the information. Heuristic methods are often the only solution. This chapter shows the impact and capabilities of alternate heuristic models, and compares their performances in auction trading.

Logic-based reasoning about actions and plans in artificial intelligence

1993 ◽  
Vol 8 (2) ◽  
pp. 91-120 ◽  
Author(s):  
Huaming Lee ◽  
James Tannock ◽  
Jon Sims Williams
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Intelligent Agents ◽  
Major Research ◽  
Theoretical Frameworks ◽  
Reasoning About Actions ◽  
Domain Independent ◽  
Rational Behaviour ◽  
Research Domain

AbstractReasoning about actions and plans is a vital aspect of the rational behaviour of intelligent agents, and hence represents a major research domain in artificial intelligence. Much work has been undertaken to develop logic-based formalisms and problem solving procedures for plan representation and plan synthesis. This paper consists of a survey of various paradigms for reasoning about actions and plans in artificial intelligence. Attention is focused on the logic-based theoretical frameworks which have built a formal foundation for the domain-independent approaches to the general principles of reasoning about actions and plans.

Cooperative Spatial Reasoning for Image Understanding

1997 ◽  
Vol 11 (01) ◽  
pp. 205-227 ◽  
Author(s):  
Takashi Matsuyama ◽  
Toshikazu Wada
Keyword(s):  
Problem Solving ◽  
Intelligent Agents ◽  
Spatial Reasoning ◽  
Spatial Information ◽  
Spatial Relations ◽  
Image Understanding ◽  
Aerial Image ◽  
Region Segmentation ◽  
Distributed Problem Solving ◽  
Cooperative Distributed Problem Solving

Spatial Reasoning, reasoning about spatial information (i.e. shape and spatial relations), is a crucial function of image understanding and computer vision systems. This paper proposes a novel spatial reasoning scheme for image understanding and demonstrates its utility and effectiveness in two different systems: region segmentation and aerial image understanding systems. The scheme is designed based on a so-called Multi-Agent/Cooperative Distributed Problem Solving Paradigm, where a group of intelligent agents cooperate with each other to fulfill a complicated task. The first part of the paper describes a cooperative distributed region segmentation system, where each region in an image is regarded as an agent. Starting from seed regions given at the initial stage, region agents deform their shapes dynamically so that the image is partitioned into mutually disjoint regions. The deformation of each individual region agent is realized by the snake algorithm14 and neighboring region agents cooperate with each other to find common region boundaries between them. In the latter part of the paper, we first give a brief description of the cooperative spatial reasoning method used in our aerial image understanding system SIGMA. In SIGMA, each recognized object such as a house and a road is regarded as an agent. Each agent generates hypotheses about its neighboring objects to establish spatial relations and to detect missing objects. Then, we compare its reasoning method with that used in the region segmentation system. We conclude the paper by showing further utilities of the Multi-gent/Cooperative Distributed Problem Solving Paradigm for image understanding.

scholarly journals Bridging the Left - Right Political Divide with Artificial Intelligence and Collective Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Cognitive Bias ◽  
Intelligent Agents ◽  
Collective Intelligence ◽  
Optimal Solution ◽  
Functional Modeling ◽  
Political Debate ◽  
The Individual ◽  
Political Divide

A model of cognition suggests that the left vs right political debate is unsolvable. However the same model also suggests that a form of collective cognition (General Collective Intelligence or GCI) can allow education, health care, or other government services to be customized to the individual, so that individuals can choose services anywhere along the spectrum from socialized services if they desire, or private services if they desire, thereby removing any political stalemate where it might prevent any progress. Whatever services groups of individuals choose, GCI can significantly increase the quality of outcomes achievable through either socialized or private services today, in part through using information regarding the fitness of any services deployed, to improve the fitness of all services that might be deployed. The emerging field of General Collective Intelligence (GCI) explores how platforms might increase the general problem-solving ability (intelligence) of groups so that it is significantly higher than that of any individual. Where Collective Intelligence (CI) must find the optimal solution to a problem or group of problems, having general problem-solving ability, a GCI must also have the capacity to find the optimal problem to solve. In the case of political discussions, GCI must have the ability to re-frame political discourse from being focused on questions that have not proved resolvable, such as whether or not left leaning or right leaning political opinions are in general more “right” or “wrong”. Instead GCI must have the ability to refocus discussions, including on how to objectively determine whether a left or right bias optimizes outcomes in a specific context, and why. This paper explores the conjecture that determining whether a left leaning or right leaning cognitive bias is "optimal" (i.e. "true) based on any CI or other aggregate of individual reasoning that is not GCI, cannot reliably converge on "truth" because each individual cognitive bias leads to evaluating truth according to different reasoning types (type 1 or type 2) that might give conflicting answers to the same problem. However, through using functional modeling to create the capacity to represent all possible reasoning processes, and through using functional modeling to represent the domains in conceptual space in which each reasoning process is optimal, it is possible to systematically categorize an unlimited number of collective reasoning processes and the contexts in which execution of those reasoning processes with a right leaning or left leaning bias is optimal for the group. By designing GCI algorithms to incorporate each bias in its optimal context, a GCI can allow individuals to participate in collective reasoning despite their biases, while collective reasoning might still converge on "truth" in terms of functioning to optimize collective outcomes. And by deploying intelligent agents incorporating some subset of AGI to interact on the individual's behalf at significantly higher speed and scale, collective reasoning might gain the capacity to consider all reasoning and all "facts" available to any individual in the group, in order to converge on that truth while significantly increasing outcomes.

scholarly journals 9/11 and the emergence of Critical Terrorism Studies

2021 ◽  
Vol 27 (2) ◽  
pp. 9-25
Author(s):  
Raquel Da Silva ◽  
Alice Martini
Keyword(s):  
Problem Solving ◽  
War On Terror ◽  
Global War On Terror ◽  
Counter Terrorism ◽  
Counter Narrative ◽  
Diverse Backgrounds ◽  
Alternative Approaches ◽  
Global War ◽  
The Impact

The attacks of 11 September 2001 have profoundly impacted the field of terrorism studies. In this article we aim to trace, in particular, the impact of this date on the establishment of critical terrorism studies (CTS) as a school of thought. Such an endeavour aims to create an ‘umbrella-term’ to gather scholars from diverse backgrounds, in an attempt to provide a counter-narrative to the dominant, mainstream understanding of terrorism and counter-terrorism. CTS scholarship offers alternative approaches to state-centred, ahistorical, and ‘problem-solving’ standpoints, which have been at the origin of numerous atrocities committed, for example, under the Global War on Terror banner. This article explores the key debates stirred by CTS scholarship over the years, its recent advancements, and existing gaps.

Hierarchical Factor Patterns on the WISC as Related to Areas of Learning Deficit

1976 ◽  
Vol 42 (2) ◽  
pp. 583-615 ◽  
Author(s):  
Peggy T. Ackerman ◽  
Roscoe A. Dykman ◽  
John E. Peters
Keyword(s):  
Problem Solving ◽  
Spatial Reasoning ◽  
Learning Disabled ◽  
Factor Scores ◽  
Initial Attempt ◽  
New Information ◽  
Sequential Memory ◽  
Alternative Approaches ◽  
Successful Students

Initial and 4-yr. follow-up findings on 62 learning disabled boys and 31 matched controls indicated that level and hierarchy of WISC factor scores (verbal reasoning, spatial reasoning, and sequential memory) were related to degree and pervasiveness of basic skill retardation. Of particular significance were scores on the sequential memory factor and the Information subtest. While students with deficits on these tests and deficiencies in mathematics, reading or in other areas, generally cannot or will not employ strategies for memorization and problem solving, they do learn adequately those meaningful (to them) life skills which can be achieved non-deliberately and without resort to strategies. The most successful students appear to use such strategies as chunking, elaboration, rehearsal, and rearrangement to encode new information, and, perhaps just as important, they are willing to try alternative approaches to problem solving when the initial attempt fails.

Alternative Approaches to Auction Trading by Consortia in Multi Agent Systems

2012 ◽  
pp. 1637-1649
Author(s):  
Barin N. Nag ◽  
Dong-Qing Yao ◽  
Sungchul Hong
Keyword(s):  
Intelligent Agents ◽  
Heuristic Methods ◽  
Multi Agent Systems ◽  
Management Activity ◽  
Chain Management ◽  
Agent Based ◽  
Agent Systems ◽  
Multi Agent ◽  
Alternative Approaches ◽  
The Impact

Agent-based auction trading is important in e-Procurement as a part of the supply chain management activity of procurement via the Internet. Participating buyers and sellers are intelligent agents tasked with finding matches with required or offered quantities for best performance. Formation of consortiums offers opportunities in matching trade volumes, but in the real world, there are difficulties in optimizing consortium formation due to lack of perfect information and the dynamic character of the information. Heuristic methods are often the only solution. This chapter shows the impact and capabilities of alternate heuristic models, and compares their performances in auction trading.

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