Nils J. Nilsson's research while affiliated with Stanford University and other places
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Publications (28)
Machine Intelligence 14 contains material presented at the Anglo-Janpanese workshop of Novemver 1993 held at the Hitachi Research Laboratory. It marks the 70th birthday of Donald Michie, the founder of the series. The contents is divided into the following subjects: complex decision taking, inductive logic programming, applied machine learning, dyn...
In its early stages, the field of artificial intelligence (AI) had as its main goal the invention of computer programs having the general problem solving abilities of humans. Along the way, there developed a major shift of emphasis from general-purpose programs toward "performance programs"---ones whose competence was highly specialized and limited...
A formalism is presented for computing and organizing actions for autonomous agents in dynamic environments. We introduce the notion of teleo-reactive (T-R) programs whose execution entails the construction of circuitry for the continuous computation of the parameters and conditions on which agent action is based. In addition to continuous feedback...
New ideas are presented for computing and organizing actions for autonomous agents in dynamic environments-environments in which the agent's current situation cannot always be accurately discerned and in which the effects of actions cannot always be reliably predicted. The notion of 'circuit semantics' for programs based on 'teleo-reactive trees' i...
The theoretical foundations of the logical approach to artificial intelligence are presented. Logical languages are widely used for expressing the declarative knowledge needed in artificial intelligence systems. Symbolic logic also provides a clear semantics for knowledge representation languages and a methodology for analyzing and comparing deduct...
Because many artificial intelligence applications require the ability to reason with uncertain knowledge, it is important to seek appropriate generalizations of logic for that case. We present here a semantical generalization of logic in which the truth values of sentences are probability values (between 0 and 1). Our generalization applies to any...
Charles A. Rosen came to SRI in 1957. I arrived in 1961. Between these dates, Charlie organized an Applied Physics Laboratory and became interested in "learning machines" and "self-organizing systems." That interest launched a group that ultimately grew into a major world center of artificial intelligence research - a center that has endured twenty...
Artificial Intelligence, as a maturing scientific/engineering discipline, is beginning to find its niche among the variety of subjects that are relevant to intelligent, perceptive behavior. A view of AI is presented that is based on a declarative representation of knowledge with seman- tic attachments to problem-specific procedures and data structu...
In this paper we describe some major new additions to the STRIPS robot problem-solving system. The first addition is a process for generalizing a plan produced by STRIPS so that problem-specific constants appearing in the plan are replaced by problem-independent parameters.
The general problem of drawing inferences from uncertain or incomplete evidence has invited a variety of technical approaches, some mathematically rigorous and some largely informal and intuitive. Most current inference systems in artificial intelligence have emphasized intuitive methods, because the absence of adequate statistical samples forces a...
Rule-based inference systems allow judgmental knowledge about a specific problem domain to be represented as a collection of discrete rules. Each rule states that if certain premises are known, then certain conclusions can be inferred. An important design issue concerns the representational form for the premises and conclusions of the rules. We des...
The general problem of drawing inferences from uncertain or incomplete evidence has invited a variety of technical approaches, some mathematically rigorous and some largely informal and intuitive. Most current inference systems in artificial intelligence have emphasized intuitive methods, because the absence of adequate statistical samples forces a...
In this paper we describe some major new additions to the STRIPS robot problem-solving system. The first addition is a process for generalizing a plan produced by SFRIPS so that problem-specific constants appearing in the plan are replaced by problem-independent para. meters. The genera!ized plan, stored m a convenient format called a triangle tabl...
The report describes activities during the most recent year of a program of research in artificial intelligence. During the year a number of experiments were conducted with an existing system for the control of a robot that autonomously plans, learns, and carries out tasks in a real laboratory environment. Designs for a new robot system were also e...
Our paper on the use of heuristic information in graph searching defined a path-finding algorithm, A*, and proved that it had two important properties. In the notation of the paper, we proved that if the heuristic function ñ (n) is a lower bound on the true minimal cost from node n to a goal node, then A* is admissible; i.e., it would find a minima...
In this paper we describe some major new additions to the STRIPS robot problem-solving system. The first addition is a process for generalizing a plan produced by STRIPS so that problem-specific constants appearing in the plan are replaced by problem-independent parameters. The generalized plan, stored in a convenient format called a triangle table...
We describe a new problem solver called STRIPS that attempts to find a sequence of operators in a space of world models to transform a given initial world model in which a given goal formula can be proven to be true. STRIPS represents a world model as an arbitrary collection in first-order predicate calculus formulas and is designed to work with mo...
Procedures for generating proofs within a logical inference system can be applied to many information-retrieval and automatic problem-solving tasks. These applications require additional procedures for extracting information from the proofs when they are found. We present an extraction procedure for proofs generated by the resolution principle. The...
Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be...
Citations
... 163 Facebook 'wants to make its Messenger app the go-to place for people to have conversations with brand's virtual ambassadors'. 164 Facebook has created the wit.ai bot engine, which allows brands to train bots with sample conversations and have these bots continually learn from customer interactions. 165 Giselle Abramovich claims that chatbots are probably 'the most common AI-powered customer service application today … To date, bots have predominantly been used to provide search and discovery and product recommendations'. ...
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... In recent years, artificial intelligence (AI) has got rapid development and allowed investigators to build a suitable model that simulate human behavior to solve the complex problems with limited data [10]. The AI method has been applied in different scientific and engineering fields such as optical sensing [11], material science [12], manufacturing industry [13], and biomedicine [14]. ...
... In recent studies, different strategies have been proposed to optimize the path planning for mobile robots based on A * algorithm. For instance, Bennewitz [4] developed a decoupling-based path planning strategy that applies A * algorithm [5] to minimize the overall path length. Guo's approach [6] incorporates speed planning to achieve dynamic path planning with real-time obstacle avoidance [7], while Li introduced the concept of Artificial Untraversable Vertex in the D * Lite method for fast replanning [8]. ...
... Macro-actions are a form of temporal abstraction to tackle long-horizon planning, by reducing the planning depth linearly and thus the planning complexity exponentially. For deterministic planning and MDPs, a macro-action can be represented as a sequence of primitive actions or a local trajectory [6,10,16,37]. Such trajectories can be extracted from past experience, typically generated by planning using primitive actions. ...
... Our goal in this study is to better understand how the knowledge needed for rapid action-sequence planning might be stored and processed in the brain. To this end, we develop a spiking-neuron model that plans action sequences by chaining together action preconditions and effects (Fikes & Nilsson, 1971 ) while interacting with a simulated environment . Each planning step selects from actions that are related to available objects, in order to constrain each decision and allow planning to proceed quickly (about 100ms of simulated time per step). ...
... It returns an estimate of the shortest (optimal) path cost from s to one of the goal states (states that satisfy G), typically through a symbolic, non-statistical means including problem relaxation and abstraction. Notable state-of-the-art functions include h LMcut , h FF , h max , h add , h GC Helmert & Domshlak (2009);Hoffmann & Nebel (2001); Bonet & Geffner (2001);Fikes et al. (1972). The optimal cost to go, or a perfect heuristic, is denoted by h * . ...
... With an AI that does not need to take a rest and sleep, the probabilities of error are almost nil and that leads to perfection and faster production. In other words, AI will minimize the cost of living because it reduces the need for human manpower, thus reducing operational costs [16,18]. ...
... These techniques are the simplest form of artificial intelligence and mimic the reasoning of a human expert in solving a knowledge-intensive problem. In other words, AI-RBSs encode human expert knowledge about a specific topic into an automated system; they are often used to comprise an expert system [43,44]. An AI-RBS reproduces deductive reasoning mechanisms by employing logic rules made of conjunctions of conditions to verify a set of actions to execute [45]. ...
... One of the methods of obtaining information under conditions of uncertainty is probabilistic logic and information modeling. The term "probabilistic logic" was first used in an article by Niels Nilsson, published in 1986, where the true values of sentences are probabilities [3]. There are many subsequent implementations of probabilistic logic. ...
Reference: Probabilistic logic in computer science
... In this section we cover only terminology used in the paper, and further confined largely to logic programming. For additional background and further terminology see Lloyd (2012), Chang and Lee (2014), Nilsson (1991), Muggleton and de Raedt (1994). The summary below is adapted from Srinivasan et al. (2019). ...
