Causal Knowledge Network Integration for Life Cycle Assessment

AAAI Conferences

Sustainability requires emphasizing the importance of environmental causes and effects among design knowledge from heterogeneous stakeholders to make a sustainable decision. Recently, such causes and effects have been well developed in ontological representation, which has been challenged to generate and integrate multiple domain knowledge due to its domain specific characteristics. Moreover, it is too challengeable to represent heterogeneous, domain-specific design knowledge in a standardized way. Causal knowledge can meet the necessity of knowledge integration in domains. Therefore, this paper aims to develop a causal knowledge integration system with the authors’ previous mathematical causal knowledge representation.


Automatic Identification of Quasi-Experimental Designs for Scientific Discovery

AAAI Conferences

We briefly describe recent research on the automatic identification of quasi-experimental designs, a family of methods used in the medical, social, and economic sciences to discover causal knowledge from observational data. These methods are widely used for manual discovery, but recent advances in knowledge representation and databases have made it possible to automate aspects of their use. We report on a prototype system for automatically identifying quasiexperimental designs and suggest future work.


AAAI97-071.pdf

AAAI Conferences

For many commonsense reasoning tasks associated with action domains, only a relatively simple kind of causal knowledge (previously studied by Geffner and Lin) is required. We define a mathematically simple language for expressing knowledge of this kind and describe a general approach to formalizing action domains in it. The language can be used to express ramification and qualification constraints, explicit definitions, concurrency, nondeterminism, and dynamic domains in which things change by themselves. It has always been clear that causal knowledge plays a central role in commonsense reasoning about actions. However, it has not always been clear what this role is, or that it cannot be played by noncausal knowledge as well.


Integrating Case-Based and Model-Based Reasoning

AI Magazine

It first reviews the core issues in experiencebased design, for example, (1) the content of a design experience (or case), (2) the internal organization of design cases, (3) the language for indexing the cases, (4) the mechanism for retrieving a case relevant to a given design task, (5) the mechanism for adapting a retrieved design to satisfy the constraints of the design task, (6) the mechanism for evaluating a design against the specification of the design task, (7) the mechanism for redesigning a failed design, (8) the mechanism for acquiring new design knowledge, (9) the mechanism for chunking information about a design into a new case, and (10) the mechanism for storing a new case in memory for potential reuse in the future. It then proposes that decisions about these issues might lie in the designer's comprehension of the designs of artifacts he/she has encountered in the past, that is, in his/her mental models of how the designs achieve the functions and satisfy the constraints of the artifacts. To elaborate and evaluate this proposal, the dissertation analyzes the design of physical devices such as simple electric circuits, heat exchangers, and angular momentum controllers. It develops a theory of designers' comprehension of device designs in terms of functional models of how devices work. The functional model of a device provides a causal explanation of how the structure of the device produces its functions.


Integrating Case-Based and Model-Based Reasoning: A Computational Model of Design Problem Solving

AI Magazine

My Ph.D. dissertation (Goel 1989) presents a computational model of experience-based design. It first reviews the core issues in experience-based design, for example, (1) the content of a design experience (or case), (2) the internal organization of design cases, (3) the language for indexing the cases, (4) the mechanism for retrieving a case relevant to a given design task, (5) the mechanism for adapting a retrieved design to satisfy the constraints of the design task, (6) the mechanism for evaluating a design against the specification of the design task, (7) the mechanism for redesigning a failed design, (8) the mechanism for acquiring new design knowledge, (9) the mechanism for chunking information about a design into a new case, and (10) the mechanism for storing a new case in memory for potential reuse in the future. It then proposes that decisions about these issues might lie in the designer's comprehension of the designs of artifacts he/she has encountered in the past, that is, in his/her mental models of how the designs achieve the functions and satisfy the constraints of the artifacts.