This article discusses building a computable design process model, which is a prerequisite for realizing intelligent computer-aided design systems. First, we introduce general design theory, from which a descriptive model of design processes is derived. In this model, the concept of metamodels plays a crucial role in describing the evolutionary nature of design. Second, we show a cognitive design process model obtained by observing design processes using a protocol analysis method. We then discuss a computable model that can explain most parts of the cognitive model and also interpret the descriptive model. In the computable model, a design process is regarded as an iterative logical process realized by abduction, deduction, and circumscription. We implemented a design simulator that can trace design processes in which design specifications and design solutions are gradually revised as the design proceeds.
Planning in real-time offers several benefits over the more typical techniques of implementing Non-Player Character (NPC) behavior with scripts or finite state machines. NPCs that plan their actions dynamically are better equipped to handle unexpected situations. The modular nature of the goals and actions that make up the plan facilitates reuse, sharing, and maintenance of behavioral building blocks. These benefits, however, come at the cost of CPU cycles. In order to simultaneously plan for several NPCs in real-time, while continuing to share the processor with the physics, animation, and rendering systems, careful consideration must taken with the supporting architecture. The architecture must support distributed processing and caching of costly calculations. These considerations have impacts that stretch beyond the architecture of the planner, and affect the agent architecture as a whole. This paper describes lessons learned while implementing real-time planning for NPCs for F.E.A.R., a AAA first person shooter shipping for PC in 2005.
The IJCAI-09 Workshop on Learning Structural Knowledge from Observations (STRUCK-09) took place as part of the International Joint Conference on Artificial Intelligence (IJCAI-09) on July 12 in Pasadena, California. The workshop program included paper presentations, discussion sessions about those papers, group discussions about two selected topics, and a joint discussion. As a result, many cognitive architectures use structural models to represent relations between knowledge of different complexity. Structural modeling has led to a number of representation and reasoning formalisms including frames, schemas, abstractions, hierarchical task networks (HTNs), and goal graphs among others. These formalisms have in common the use of certain kinds of constructs (for example, objects, goals, skills, and tasks) that represent knowledge of varying degrees of complexity and that are connected through structural relations.
Northrop Research and Technology Center, One Research Park, Pales Wdes Peninsula, CA 90274 It, is interesting t,o note that there is no agreed upon definition of artificial intrlligence. Because government agencies ask for it, software shops claim to provide it, popular magazines and newspapers publish articles about, it, dreamers base their fant,asies on it, and pragmatists criticize and denounce it. Such a stat,c of affairs has persisted since Newell, Simon, and Shaw wrote thcif first. Not knowing exactly what we ale talking about, or expecting is typical of a new field; for example, witness the chaos that centcrcd around program verification of security rclated aspects of systems a few years ago The details are too glim to recount, in mixed company. However, artificial intelligence has been around for nearly 30 years, so one might wonder why our wheels are st,ill spinning.
Humans are already forming relationships with their artificial intelligence (AI) assistants, so we should make that technology as emotionally aware as possible by teaching it to respond to our feelings. That is the premise of Rana el Kaliouby, cofounder and CEO of Affectiva, an MIT spinout company that sells emotion recognition technology based on her computer science PhD, which she spent building the first ever computer that can recognise emotions. The machine learning-based software uses a camera or webcam to identify parts of human faces (eyebrows, the corners of eyes, etc), classify expressions and map them onto emotions like joy, disgust, surprise, anger, and so on, in real time. "We are getting lots of interest around chatbots, self-driving cars, anything with a conversational interface. If it's interfacing with a human it needs social and emotional skills.